Han Yu

AAAI Conference 2026 Conference Paper

Enhancing Logical Expressiveness in Graph Neural Networks via Path-Neighbor Aggregation

• Han Yu
• Xiaojuan Zhao
• Aiping Li
• Kai Chen
• Ziniu Liu
• Zhichao Peng

Graph neural networks (GNNs) can effectively model structural information of graphs, making them widely used in knowledge graph (KG) reasoning. However, existing studies on the expressive power of GNNs mainly focuses on simple single-relation graphs, and there is still insufficient discussion on the power of GNN to express logical rules in KGs. How to enhance the logical expressive power of GNNs is still a key issue. Motivated by this, we propose Path-Neighbor enhanced GNN (PN-GNN), a method to enhance the logical expressive power of GNN by aggregating node-neighbor embeddings on the reasoning path. First, we analyze the logical expressive power of existing GNN-based methods and point out the shortcomings of the expressive power of these methods. Then, we theoretically investigate the logical expressive power of PN-GNN, showing that it not only has strictly stronger expressive power than C-GNN but also that its (k+1)-hop logical expressiveness is strictly superior to that of k-hop. Finally, we evaluate the logical expressive power of PN-GNN on six synthetic datasets and two real-world datasets. Both theoretical analysis and extensive experiments confirm that PN-GNN enhances the expressive power of logical rules without compromising generalization, as evidenced by its competitive performance in KG reasoning tasks.

AAAI Conference 2026 Conference Paper

Error Slice Discovery via Manifold Compactness

• Han Yu
• Hao Zou
• Jiashuo Liu
• Renzhe Xu
• Yue He
• Xingxuan Zhang
• Peng Cui

Despite the great performance of deep learning models in many areas, they still make mistakes and underperform on certain subsets of data, i.e. error slices. Given a trained model, it is important to identify its semantically coherent error slices that are easy to interpret, which is referred to as the error slice discovery problem. However, there is no proper metric of slice coherence without relying on extra information like predefined slice labels. Current evaluation of slice coherence requires access to predefined slices formulated by metadata like attributes or subclasses. Its validity heavily relies on the quality and abundance of metadata, where some possible patterns could be ignored. Besides, current algorithms cannot directly incorporate the constraint of coherence into their optimization objective due to absence of an explicit coherence metric, which could potentially hinder their effectiveness. In this paper, we propose manifold compactness, a coherence metric without reliance on extra information by incorporating the data geometry property into its design, and experiments on typical datasets empirically validate the rationality of the metric. Then we develop Manifold Compactness based error Slice Discovery (MCSD), a novel algorithm that directly treats risk and coherence as the optimization objective, and is flexible to be applied to models of various tasks. Extensive experiments on the benchmark and case studies on other typical datasets demonstrate the superiority of MCSD.

AAAI Conference 2026 System Paper

Federated Learning Playground

• Bryan Shan Guanrong
• Alysa Ziying Tan
• Han Yu

We present Federated Learning Playground, an interactive browser-based platform inspired by and extends TensorFlow Playground that teaches core Federated Learning (FL) concepts. Users can experiment with heterogeneous client data distributions, model hyperparameters, and aggregation algorithms directly in the browser without coding or system setup, and observe their effects on client and global models through real-time visualizations, gaining intuition for challenges such as non-IID data, local overfitting, and scalability. The playground serves as an easy to use educational tool, lowering the entry barrier for newcomers to distributed AI while also offering a sandbox for rapidly prototyping and comparing FL methods. By democratizing exploration of FL, it promotes broader understanding and adoption of this important paradigm.

AAAI Conference 2026 Conference Paper

Generating Risky Samples with Conformity Constraints via Diffusion Models

• Han Yu
• Hao Zou
• Xingxuan Zhang
• Zhengyi Wang
• Yue He
• Kehan Li
• Peng Cui

Although neural networks achieve promising performance in many tasks, they may still fail when encountering some examples and bring about risks to applications. To discover risky samples, previous literature attempts to search for patterns of risky samples within existing datasets or inject perturbation into them. Yet in this way the diversity of risky samples is limited by the coverage of existing datasets. To overcome this limitation, recent works adopt diffusion models to produce new risky samples beyond the coverage of existing datasets. However, these methods struggle in the conformity between generated samples and expected categories, which could introduce label noise and severely limit their effectiveness in applications. To address this issue, we propose RiskyDiff that incorporates the embeddings of both texts and images as implicit constraints of category conformity. We also design a conformity score to further explicitly strengthen the category conformity, as well as introduce the mechanisms of embedding screening and risky gradient guidance to boost the risk of generated samples. Extensive experiments reveal that RiskyDiff greatly outperforms existing methods in terms of the degree of risk, generation quality, and conformity with conditioned categories. We also empirically show the generalization ability of the models can be enhanced by augmenting training data with generated samples of high conformity.

NeurIPS Conference 2025 Conference Paper

A Reinforcement Learning-based Bidding Strategy for Data Consumers in Auction-based Federated Learning

• Xiaoli Tang
• Han Yu
• Xiaoxiao Li

Auction-based Federated Learning (AFL) fosters collaboration among self-interested data consumers (DCs) and data owners (DOs). A major challenge in AFL pertains to how DCs select and bid for DOs. Existing methods are generally static, making them ill-suited for dynamic AFL markets. To address this issue, we propose the R}einforcement Learning-based Bidding Strategy for DCs in Auction-based Federated Learning (RLB-AFL). We incorporate historical states into a Deep Q-Network to capture sequential information critical for bidding decisions. To mitigate state space sparsity, where specific states rarely reoccur for each DC during auctions, we incorporate the Gaussian Mixture Model into RLB-AFL. This facilitates soft clustering on sequential states, reducing the state space dimensionality and easing exploration and action-value function approximation. In addition, we enhance the $\epsilon$-greedy policy to help the RLB-AFL agent balance exploitation and exploration, enabling it to be more adaptable in the AFL decision-making process. Extensive experiments under 6 widely used benchmark datasets demonstrate that RLB-AFL achieves superior performance compared to 8 state-of-the-art approaches. It outperforms the best baseline by 10. 56% and 3. 15% in terms of average total utility

TMLR Journal 2025 Journal Article

Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models

• Zeyu Yang
• Han Yu
• Peikun Guo
• Khadija Zanna
• Xiaoxue Yang
• Akane Sano

Diffusion models have emerged as a robust framework for various generative tasks, including tabular data synthesis. However, current tabular diffusion models tend to inherit bias in the training dataset and generate biased synthetic data, which may influence discriminatory actions. In this research, we introduce a novel tabular diffusion model that incorporates sensitive guidance to generate fair synthetic data with balanced joint distributions of the target label and sensitive attributes, such as sex and race. The empirical results demonstrate that our method effectively mitigates bias in training data while maintaining the quality of the generated samples. Furthermore, we provide evidence that our approach outperforms existing methods for synthesizing tabular data on fairness metrics such as demographic parity ratio and equalized odds ratio, achieving improvements of over $10\%$. Our implementation is available at https://github.com/comp-well-org/fair-tab-diffusion.

NeurIPS Conference 2025 Conference Paper

Class-wise Balancing Data Replay for Federated Class-Incremental Learning

• Zhuang Qi
• Ying-Peng Tang
• Lei Meng
• Han Yu
• Xiaoxiao Li
• Xiangxu Meng

Federated Class Incremental Learning (FCIL) aims to collaboratively process continuously increasing incoming tasks across multiple clients. Among various approaches, data replay has become a promising solution, which can alleviate forgetting by reintroducing representative samples from previous tasks. However, their performance is typically limited by class imbalance, both within the replay buffer due to limited global awareness and between replayed and newly arrived classes. To address this issue, we propose a class-wise balancing data replay method for FCIL (FedCBDR), which employs a global coordination mechanism for class-level memory construction and reweights the learning objective to alleviate the aforementioned imbalances. Specifically, FedCBDR has two key components: 1) the global-perspective data replay module reconstructs global representations of prior task knowledge in a privacy-preserving manner, which then guides a class-aware and importance-sensitive sampling strategy to achieve balanced replay; 2) Subsequently, to handle class imbalance across tasks, the task-aware temperature scaling module adaptively adjusts the temperature of logits at both class and instance levels based on task dynamics, which reduces the model’s overconfidence in majority classes while enhancing its sensitivity to minority classes. Experimental results verified that FedCBDR achieves balanced class-wise sampling under heterogeneous data distributions and improves generalization under task imbalance between earlier and recent tasks, yielding a 2%-15% Top-1 accuracy improvement over six state-of-the-art methods.

NeurIPS Conference 2025 Conference Paper

CrossSpectra: Exploiting Cross-Layer Smoothness for Parameter-Efficient Fine-Tuning

• Yifei Zhang
• Hao Zhu
• Junhao Dong
• Haoran Shi
• Ziqiao Meng
• Piotr Koniusz
• Han Yu

Parameter-efficient fine-tuning (PEFT) is essential for adapting large foundation models without excessive storage cost. However, current approaches such as LoRA treat each layer’s adaptation independently, overlooking correlations across layers. This independence causes the number of trainable parameters to grow linearly with model depth. We provide theoretical and empirical evidence that skip connections in transformers create smooth gradient propagation across layers. This smoothness leads to weight adaptations that concentrate most of their energy in low-frequency spectral components, especially along the layer dimension. Empirical analysis confirms this effect, showing that most of adaptation energy lies in low frequencies. Building on this insight, we propose CrossSpectra, which parameterizes all attention-weight adaptations $(Q, K, V)$ across layers as a single 3D tensor and represents them with sparse spectral coefficients ($\kappa_1, \kappa_2$). Using $\kappa_{1}$ non-zero coefficients within each layer’s frequency space and truncating to $\kappa_{2}$ frequencies across layers, CrossSpectra requires $\mathcal{O}(\kappa_{1}\kappa_{2})$ parameters instead of LoRA’s $\mathcal{O}(Lrd)$, where $L$ is the number of layers and $r$ the rank. Across natural-language and vision benchmarks, \methodname{} matches or surpasses baseline performance while using fewer parameters than LoRA, achieving only $0. 36\%$ of LoRA’s parameter count when fine-tuning LLaMA-7B on instruction-following tasks. These results show that exploiting the \textbf{architectural smoothness of transformers} through spectral analysis yields major efficiency gains in PEFT.

EAAI Journal 2025 Journal Article

Dynamic masking-based feature interaction modeling for e-commerce click-through rate prediction

• Yabo Ni
• Yueqiu Wu
• Jingyi Li
• Anxiang Zeng
• Han Yu
• Xiaoxiao Li

AAAI Conference 2025 Conference Paper

Federated Causally Invariant Feature Learning

• Xianjie Guo
• Kui Yu
• Lizhen Cui
• Han Yu
• Xiaoxiao Li

Federated feature selection (FFS) is a promising field for selecting informative features while preserving data privacy in federated learning (FL) settings. Existing FFS methods focus on capturing the correlations between features and labels. They struggle to achieve satisfactory performance in the face of data distribution heterogeneity among FL clients, and cannot address the out-of-distribution (OOD) problem that arises when a significant portion of clients do not actively participate in FL training. To address these limitations, we propose Federated Causally Invariant Feature Learning (FedCIFL), a novel approach for learning causally invariant features in a privacy-preserving manner. We design a sample reweighting strategy to eliminate spurious correlations introduced by selection bias and iteratively estimate the federated causal effect between each feature and the labels (with the remaining features initially treated as confounders). By iteratively refining the confounding feature set to identify the true confounders, FedCIFL mitigates the impact of limited local data on the accuracy of federated causal effect estimation. Theoretical analysis proves the correctness of FedCIFL under reasonable assumptions. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of FedCIFL against eight state-of-the-art baselines, beating the best-performing approach by 3.19%, 9.07% and 2.65% in terms of average test Accuracy, RMSE and F1 score, respectively. It is a first-of-its-kind FFS approach capable of handling Non-IID and OOD data simultaneously. The source code is available at https://github.com/Xianjie-Guo/FedCIFL.

IJCAI Conference 2025 Conference Paper

Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization

• Zhuang Qi
• Sijin Zhou
• Lei Meng
• Han Hu
• Han Yu
• Xiangxu Meng

Attribute bias in federated learning (FL) typically leads local models to optimize inconsistently due to the learning of non-causal associations, resulting degraded performance. Existing methods either use data augmentation for increasing sample diversity or knowledge distillation for learning invariant representations to address this problem. However, they lack a comprehensive analysis of the inference paths, and the interference from confounding factors limits their performance. To address these limitations, we propose the Federated Deconfounding and Debiasing Learning (FedDDL) method. It constructs a structured causal graph to analyze the model inference process, and performs backdoor adjustment to eliminate confounding paths. Specifically, we design an intra-client deconfounding learning module for computer vision tasks to decouple background and objects, generating counterfactual samples that establish a connection between the background and any label, which stops the model from using the background to infer the label. Moreover, we design an inter-client debiasing learning module to construct causal prototypes to reduce the proportion of the background in prototype components. Notably, it bridges the gap between heterogeneous representations via causal prototypical regularization. Extensive experiments on 2 benchmarking datasets demonstrate that FedDDL significantly enhances the model capability to focus on main objects in unseen data, leading to 4. 5% higher Top-1 Accuracy on average over 9 state-of-the-art existing methods.

NeurIPS Conference 2025 Conference Paper

Global Prompt Refinement with Non-Interfering Attention Masking for One-Shot Federated Learning

• Zhuang Qi
• Yu Pan
• Lei Meng
• Sijin Zhou
• Han Yu
• Xiaoxiao Li
• Xiangxu Meng

Federated Prompt Learning (FPL) enables communication-efficient adaptation by tuning lightweight prompts on top of frozen pre-trained models. Existing FPL methods typically rely on global information, which is only available after the second training round, to facilitate collaboration among client models. Therefore, they are inherently dependent on multi-round communication to fully exhibit their strengths. Moreover, existing one-shot federated learning methods typically focus on fitting seen tasks, but lack cross-task generalization. To bridge this gap, we propose the global prompt refinement with non-interfering attention masking (GPR-NIAM) method for one-shot FPL. The core idea is to design a masking mechanism that restricts excessive interaction between the original text embeddings and the learnable prompt embeddings. GPR-NIAM achieves this through the collaboration of two key modules. Firstly, the attention isolation module suppresses attention from the learnable prompt tokens to the original text tokens, and reweights the reverse attention which preserves generalization across tasks. Secondly, the cross-silo collaborative refinement module integrates decentralized visual knowledge into a unified base and calibrates the global prompt through multi-source cross-modal knowledge alignment, further mitigating the inconsistency caused by data heterogeneity. Extensive experiments conducted on ten benchmark datasets under two tasks show that GPR-NIAM outperforms eight state-of-the-art methods in both class-level and domain-level generalization.

ICLR Conference 2025 Conference Paper

In vivo cell-type and brain region classification via multimodal contrastive learning

• Han Yu
• Hanrui Lyu
• YiXun Xu
• Charlie Windolf
• Eric Kenji Lee
• Fan Yang
• Andrew M. Shelton
• Olivier Winter

Current electrophysiological approaches can track the activity of many neurons, yet it is usually unknown which cell-types or brain areas are being recorded without further molecular or histological analysis. Developing accurate and scalable algorithms for identifying the cell-type and brain region of recorded neurons is thus crucial for improving our understanding of neural computation. In this work, we develop a multimodal contrastive learning approach for neural data that can be fine-tuned for different downstream tasks, including inference of cell-type and brain location. We utilize multimodal contrastive learning to jointly embed the activity autocorrelations and extracellular waveforms of individual neurons. We demonstrate that our embedding approach, Neuronal Embeddings via MultimOdal Contrastive Learning (NEMO), paired with supervised fine-tuning, achieves state-of-the-art cell-type classification for two opto-tagged datasets and brain region classification for the public International Brain Laboratory Brain-wide Map dataset. Our method represents a promising step towards accurate cell-type and brain region classification from electrophysiological recordings.

NeurIPS Conference 2025 Conference Paper

Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections

• Wei Zhuo
• Zhaohuan Zhan
• Han Yu

Federated Learning (FL) on graph-structured data typically faces non-IID challenges, particularly in scenarios where each client holds a distinct subgraph sampled from a global graph. In this paper, we introduce Fed erated learning with Aux iliary projections (FedAux), a personalized subgraph FL framework that learns to align, compare, and aggregate heterogeneously distributed local models without sharing raw data or node embeddings. In FedAux, each client jointly trains (i) a local GNN and (ii) a learnable auxiliary projection vector (APV) that differentiably projects node embeddings onto a 1D space. A soft-sorting operation followed by a lightweight 1D convolution refines these embeddings in the ordered space, enabling the APV to effectively capture client-specific information. After local training, these APVs serve as compact signatures that the server uses to compute inter‑client similarities and perform similarity‑weighted parameter mixing, yielding personalized models while preserving cross‑client knowledge transfer. Moreover, we provide rigorous theoretical analysis to establish the convergence and rationality of our design. Empirical evaluations across diverse graph benchmarks demonstrate that FedAux substantially outperforms existing baselines in both accuracy and personalization performance. The code is available at https: //github. com/JhuoW/FedAux.

AAAI Conference 2025 Conference Paper

pFedES: Generalized Proxy Feature Extractor Sharing for Model Heterogeneous Personalized Federated Learning

• Liping Yi
• Han Yu
• Chao Ren
• Gang Wang
• Xiaoguang Liu
• Xiaoxiao Li

Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, has attracted significant interest from industry and academia. To allow each data owner (FL client) to train a heterogeneous and personalized local model based on its local data distribution, system resources and requirements on model structure, the field of model-heterogeneous personalized federated learning (MHPFL) has emerged. Existing MHPFL approaches either rely on the availability of a public dataset with special characteristics to facilitate knowledge transfer, incur high computational and communication costs, or face potential model leakage risks. To address these limitations, we propose a model-heterogeneous personalized Federated learning approach based on generalized proxy feature Extractor Sharing (pFedES) for supervised image classification tasks. (1) We devise a shared small proxy homogeneous feature extractor before each client's heterogeneous local model. (2) Clients train them via the proposed iterative learning to enable the exchange of global generalized knowledge and local personalized knowledge. (3) The small proxy local homogeneous extractors produced after local training are uploaded to the server for aggregation to facilitate knowledge fusion across clients. We theoretically prove pFedES converges with a non-convex convergence rate O(1/T). Experiments on 3 benchmark datasets against 9 baselines demonstrate that pFedES performs state-of-the-art model accuracy while maintaining efficient communication and computation.

AAAI Conference 2025 Conference Paper

Reputation-aware Revenue Allocation for Auction-based Federated Learning

• Xiaoli Tang
• Han Yu

Auction-based Federated Learning (AFL) has gained significant research interest due to its ability to incentivize data owners (DOs) to participate in FL model training tasks of data consumers (DCs) through economic mechanisms via the auctioneer. One of the critical research issues in AFL is decision support for the auctioneer. Existing approaches are based on the simplified assumption of a single, monopolistic AFL marketplace, which is unrealistic in real-world scenarios where multiple AFL marketplaces can co-exist and compete for the same pool of DOs. In this paper, we relax this assumption and frame the AFL auctioneer decision support problem from the perspective of helping them attract participants in a competitive AFL marketplace environment while safeguarding profit. To achieve this objective, we propose the Auctioneer Revenue Allocation Strategy for AFL (ARAS-AFL). We design the concepts of the attractiveness and competitiveness from the perspective of autioneer reputation. Based on the Lyapunov optimization, ARAS-AFL helps individual AFL auctioneer achieve the dual objective of balancing the reputation management costs and its own profit by designing a dynamic revenue allocation strategy. It takes into account both the auctioneer’s revenue and the changes in the number of participants on the AFL marketplace. Through extensive experiments on widely used benchmark datasets, ARAS-AFL demonstrates superior performance compared to state-of-the-art approaches. It outperforms the best baseline by 49.06%, 98.69%, 10.32%, and 4.77% in terms of total revenue, number of data owners, public reputation and accuracy of federated learning models, respectively.

NeurIPS Conference 2025 Conference Paper

Tight High-Probability Bounds for Nonconvex Heavy-Tailed Scenario under Weaker Assumptions

• Weixin An
• Yuanyuan Liu
• Fanhua Shang
• Han Yu
• Junkang Liu
• Hongying Liu

Gradient clipping is increasingly important in centralized learning (CL) and federated learning (FL). Many works focus on its optimization properties under strong assumptions involving Gaussian noise and standard smoothness. However, practical machine learning tasks often only satisfy weaker conditions, such as heavy-tailed noise and $(L_0, L_1)$-smoothness. To bridge this gap, we propose a high-probability analysis for clipped Stochastic Gradient Descent (SGD) under these weaker assumptions. Our findings show a better convergence rate than existing ones can be achieved, and our high-probability analysis does not rely on the bounded gradient assumption. Moreover, we extend our analysis to FL, where a gap remains between expected and high-probability convergence, which the naive clipped SGD cannot bridge. Thus, we design a new \underline{Fed}erated \underline{C}lipped \underline{B}atched \underline{G}radient (FedCBG) algorithm, and prove the convergence and generalization bounds with high probability for the first time. Our analysis reveals the trade-offs between the optimization and generalization performance. Extensive experiments demonstrate that \methodname{} can generalize better to unseen client distributions than state-of-the-art baselines.

IJCAI Conference 2024 Conference Paper

A Bias-Free Revenue-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning

• Xiaoli Tang
• Han Yu
• Zengxiang Li
• Xiaoxiao Li

Auction-based Federated Learning (AFL) is a burgeoning research area. However, existing bidding strategies for AFL data consumers (DCs) primarily focus on maximizing expected accumulated utility, disregarding the more complex goal of revenue maximization. They also only consider winning bids, leading to biased estimates by overlooking information from losing bids. To address these issues, we propose a Bias-free Revenue-maximizing Federated bidding strategy for DCs in AFL (BR-FEDBIDDER). Our theoretical exploration of the relationships between Return on Investment (ROI), bid costs, and utility, and their impact on overall revenue underscores the complexity of maximizing revenue solely by prioritizing ROI enhancement. Leveraging these insights, BR-FEDBIDDER optimizes bid costs with any given ROI constraint. In addition, we incorporate an auxiliary task of winning probability estimation into the framework to achieve bias-free learning by leveraging bid records from historical bid requests, including both winning and losing ones. Extensive experiments on six widely used benchmark datasets show that BR-FEDBIDDER outperforms eight state-of-the-art methods, surpassing the best-performing baseline by 5. 66%, 6. 08% and 2. 44% in terms of the total revenue, ROI, and test accuracy of the resulting FL models, respectively.

TMLR Journal 2024 Journal Article

AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis

• Han Yu
• Peikun Guo
• Akane Sano

Time series data analysis is a critical component in various domains such as finance, healthcare, and meteorology. Despite the progress in deep learning for time series analysis, there remains a challenge in addressing the non-stationary nature of time series data. Most of the existing models, which are built on the assumption of constant statistical properties over time, often struggle to capture the temporal dynamics in realistic time series and result in bias and error in time series analysis. This paper introduces the Adaptive Wavelet Network (AdaWaveNet), a novel approach that employs Adaptive Wavelet Transformation for multi-scale analysis of non-stationary time series data. AdaWaveNet designed a lifting scheme-based wavelet decomposition and construction mechanism for adaptive and learnable wavelet transforms, which offers enhanced flexibility and robustness in analysis. We conduct extensive experiments on 10 datasets across 3 different tasks, including forecasting, imputation, and a newly established super-resolution task. The evaluations demonstrate the effectiveness of AdaWaveNet over existing methods in all three tasks, which illustrates its potential in various real-world applications.

IJCAI Conference 2024 Conference Paper

Dual Calibration-based Personalised Federated Learning

• Xiaoli Tang
• Han Yu
• Run Tang
• Chao Ren
• Anran Li
• Xiaoxiao Li

Personalized federated learning (PFL) is designed for scenarios with non-independent and identically distributed (non-IID) client data. Existing model mixup-based methods, one of the main approaches of PFL, can only extract either global or personalized features during training, thereby limiting effective knowledge sharing among clients. To address this limitation, we propose the Dual Calibration-based PFL (DC-PFL). It divides local models into a heterogeneous feature extractor and a homogeneous classifier. The FL server utilizes mean and covariance representations from clients' feature extractors to train a global generalized classifier, facilitating information exchange while preserving privacy. To enhance personalization and convergence, we design a feature extractor-level calibration method with an auxiliary loss for local models to refine feature extractors using global knowledge. Furthermore, DC-PFL refines the global classifier through the global classifier-level calibration, utilizing sample representations derived from an approximate Gaussian distribution model specific to each class. This method precludes the need to transmit original data representations, further enhancing privacy preservation. Extensive experiments on widely used benchmark datasets demonstrate that DC-PFL outperforms eight state-of-the-art methods, surpassing the best-performing baseline by 1. 22% and 9. 22% in terms of accuracy on datasets CIFAR-10 and CIFAR-100, respectively.

TMLR Journal 2024 Journal Article

ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text

• Han Yu
• Peikun Guo
• Akane Sano

The utilization of deep learning on electrocardiogram (ECG) analysis has brought the advanced accuracy and efficiency of cardiac healthcare diagnostics. In this work, we address a critical challenge in the field of ECG analysis with deep learning: learning robust representation without large-scale labeled datasets. We propose ECG Semantic Integrator (ESI), a novel multimodal contrastive pretraining framework that jointly learns from ECG signals and associated textual descriptions. ESI employs a dual objective function that comprises a contrastive loss and a captioning loss to develop representations of ECG data. To create a sufficiently large and diverse training dataset, we develop a retrieval-augmented generation (RAG)-based Large Language Model (LLM) pipeline, called Cardio Query Assistant (CQA). This pipeline is designed to generate detailed textual descriptions for ECGs from diverse databases. The generated text includes information about demographics and waveform patterns. This approach enables us to compile a large-scale multimodal dataset with over 660,000 ECG-text pairs for pretraining ESI, which then learns robust and generalizable representations of 12-lead ECG. We validate our approach through various downstream tasks, including arrhythmia detection and ECG-based subject identification. Our experimental results demonstrate substantial improvements over strong baselines in these tasks. These baselines encompass supervised and self-supervised learning methods, as well as prior multimodal pretraining approaches. Our work shows the potential of combining multimodal pretraining to improve the analysis of ECG signals.

AAAI Conference 2024 Conference Paper

FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants

• Shanli Tan
• Hao Cheng
• Xiaohu Wu
• Han Yu
• Tiantian He
• Yew Soon Ong
• Chongjun Wang
• Xiaofeng Tao

Federated learning (FL) provides a privacy-preserving approach for collaborative training of machine learning models. Given the potential data heterogeneity, it is crucial to select appropriate collaborators for each FL participant (FL-PT) based on data complementarity. Recent studies have addressed this challenge. Similarly, it is imperative to consider the inter-individual relationships among FL-PTs where some FL-PTs engage in competition. Although FL literature has acknowledged the significance of this scenario, practical methods for establishing FL ecosystems remain largely unexplored. In this paper, we extend a principle from the balance theory, namely “the friend of my enemy is my enemy”, to ensure the absence of conflicting interests within an FL ecosystem. The extended principle and the resulting problem are formulated via graph theory and integer linear programming. A polynomial-time algorithm is proposed to determine the collaborators of each FL-PT. The solution guarantees high scalability, allowing even competing FL-PTs to smoothly join the ecosystem without conflict of interest. The proposed framework jointly considers competition and data heterogeneity. Extensive experiments on real-world and synthetic data demonstrate its efficacy compared to five alternative approaches, and its ability to establish efficient collaboration networks among FL-PTs.

NeurIPS Conference 2024 Conference Paper

Federated Model Heterogeneous Matryoshka Representation Learning

• Liping Yi
• Han Yu
• Chao Ren
• Gang Wang
• Xiaoguang Liu
• Xiaoxiao Li

Model heterogeneous federated learning (MHeteroFL) enables FL clients to collaboratively train models with heterogeneous structures in a distributed fashion. However, existing MHeteroFL methods rely on training loss to transfer knowledge between the client model and the server model, resulting in limited knowledge exchange. To address this limitation, we propose the **Fed**erated model heterogeneous **M**atryoshka **R**epresentation **L**earning (**FedMRL**) approach for supervised learning tasks. It adds an auxiliary small homogeneous model shared by clients with heterogeneous local models. (1) The generalized and personalized representations extracted by the two models' feature extractors are fused by a personalized lightweight representation projector. This step enables representation fusion to adapt to local data distribution. (2) The fused representation is then used to construct Matryoshka representations with multi-dimensional and multi-granular embedded representations learned by the global homogeneous model header and the local heterogeneous model header. This step facilitates multi-perspective representation learning and improves model learning capability. Theoretical analysis shows that FedMRL achieves a $O(1/T)$ non-convex convergence rate. Extensive experiments on benchmark datasets demonstrate its superior model accuracy with low communication and computational costs compared to seven state-of-the-art baselines. It achieves up to 8. 48% and 24. 94% accuracy improvement compared with the state-of-the-art and the best same-category baseline, respectively.

IJCAI Conference 2024 Conference Paper

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

• Liping Yi
• Han Yu
• Zhuan Shi
• Gang Wang
• Xiaoguang Liu
• Lizhen Cui
• Xiaoxiao Li

Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a. k. a. FL clients) to train the same local model. This design is not well-suited for scenarios involving data and/or system heterogeneity. Model-Heterogeneous Personalized FL (MHPFL) has emerged to address this challenge. Existing MHPFL approaches often rely on a public dataset with the same nature as the learning task, or incur high computation and communication costs. To address these limitations, we propose the Federated Semantic Similarity Aggregation (FedSSA) approach for supervised classification tasks, which splits each client's model into a heterogeneous (structure-different) feature extractor and a homogeneous (structure-same) classification header. It performs local-to-global knowledge transfer via semantic similarity-based header parameter aggregation. In addition, global-to-local knowledge transfer is achieved via an adaptive parameter stabilization strategy which fuses the seen-class parameters of historical local headers with that of the latest global header for each client. FedSSA does not rely on public datasets, while only requiring partial header parameter transmission to save costs. Theoretical analysis proves the convergence of FedSSA. Extensive experiments present that FedSSA achieves up to 3. 62% higher accuracy, 15. 54 times higher communication efficiency, and 15. 52 times higher computational efficiency compared to 7 state-of-the-art MHPFL baselines.

NeurIPS Conference 2024 Conference Paper

Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning

• Mengmeng Chen
• Xiaohu Wu
• Xiaoli Tang
• Tiantian He
• Yew-Soon Ong
• Qiqi Liu
• Qicheng Lao
• Han Yu

Federated learning (FL) is a machine learning paradigm that allows multiple FL participants (FL-PTs) to collaborate on training models without sharing private data. Due to data heterogeneity, negative transfer may occur in the FL training process. This necessitates FL-PT selection based on their data complementarity. In cross-silo FL, organizations that engage in business activities are key sources of FL-PTs. The resulting FL ecosystem has two features: (i) self-interest, and (ii) competition among FL-PTs. This requires the desirable FL-PT selection strategy to simultaneously mitigate the problems of free riders and conflicts of interest among competitors. To this end, we propose an optimal FL collaboration formation strategy -FedEgoists- which ensures that: (1) a FL-PT can benefit from FL if and only if it benefits the FL ecosystem, and (2) a FL-PT will not contribute to its competitors or their supporters. It provides an efficient clustering solution to group FL-PTs into coalitions, ensuring that within each coalition, FL-PTs share the same interest. We theoretically prove that the FL-PT coalitions formed are optimal since no coalitions can collaborate together to improve the utility of any of their members. Extensive experiments on widely adopted benchmark datasets demonstrate the effectiveness of FedEgoists compared to nine state-of-the-art baseline methods, and its ability to establish efficient collaborative networks in cross-silos FL with FL-PTs that engage in business activities.

IJCAI Conference 2024 Conference Paper

Intelligent Agents for Auction-based Federated Learning: A Survey

• Xiaoli Tang
• Han Yu
• Xiaoxiao Li
• Sarit Kraus

Auction-based federated learning (AFL) is an important emerging category of FL incentive mechanism design, due to its ability to fairly and efficiently motivate high-quality data owners to join data consumers' (i. e. , servers') FL training tasks. To enhance the efficiency in AFL decision support for stakeholders (i. e. , data consumers, data owners, and the auctioneer), intelligent agent-based techniques have emerged. However, due to the highly interdisciplinary nature of this field and the lack of a comprehensive survey providing an accessible perspective, it is a challenge for researchers to enter and contribute to this field. This paper bridges this important gap by providing a first-of-its-kind survey on the Intelligent Agents for AFL (IA-AFL) literature. We propose a unique multi-tiered taxonomy that organises existing IA-AFL works according to 1) the stakeholders served, 2) the auction mechanism adopted, and 3) the goals of the agents, to provide readers with a multi-perspective view into this field. In addition, we analyse the limitations of existing approaches, summarise the commonly adopted performance evaluation metrics, and discuss promising future directions leading towards effective and efficient stakeholder-oriented decision support in IA-AFL ecosystems.

NeurIPS Conference 2024 Conference Paper

Linear Uncertainty Quantification of Graphical Model Inference

• Chenghua Guo
• Han Yu
• Jiaxin Liu
• Chao Chen
• Qi Li
• Sihong Xie
• Xi Zhang

Uncertainty Quantification (UQ) is vital for decision makers as it offers insights into the potential reliability of data and model, enabling more informed and risk-aware decision-making. Graphical models, capable of representing data with complex dependencies, are widely used across domains. Existing sampling-based UQ methods are unbiased but cannot guarantee convergence and are time-consuming on large-scale graphs. There are fast UQ methods for graphical models with closed-form solutions and convergence guarantee but with uncertainty underestimation. We propose LinUProp, a UQ method that utilizes a novel linear propagation of uncertainty to model uncertainty among related nodes additively instead of multiplicatively, to offer linear scalability, guaranteed convergence, and closed-form solutions without underestimating uncertainty. Theoretically, we decompose the expected prediction error of the graphical model and prove that the uncertainty computed by LinUProp is the generalized variance component of the decomposition. Experimentally, we demonstrate that LinUProp is consistent with the sampling-based method but with linear scalability and fast convergence. Moreover, LinUProp outperforms competitors in uncertainty-based active learning on four real-world graph datasets, achieving higher accuracy with a lower labeling budget.

AAAI Conference 2024 Conference Paper

LR-XFL: Logical Reasoning-Based Explainable Federated Learning

• Yanci Zhang
• Han Yu

Federated learning (FL) is an emerging approach for training machine learning models collaboratively while preserving data privacy. The need for privacy protection makes it difficult for FL models to achieve global transparency and explainability. To address this limitation, we incorporate logic-based explanations into FL by proposing the Logical Reasoning-based eXplainable Federated Learning (LR-XFL) approach. Under LR-XFL, FL clients create local logic rules based on their local data and send them, along with model updates, to the FL server. The FL server connects the local logic rules through a proper logical connector that is derived based on properties of client data, without requiring access to the raw data. In addition, the server also aggregates the local model updates with weight values determined by the quality of the clients’ local data as reflected by their uploaded logic rules. The results show that LR-XFL outperforms the most relevant baseline by 1.19%, 5.81% and 5.41% in terms of classification accuracy, rule accuracy and rule fidelity, respectively. The explicit rule evaluation and expression under LR-XFL enable human experts to validate and correct the rules on the server side, hence improving the global FL model’s robustness to errors. It has the potential to enhance the transparency of FL models for areas like healthcare and finance where both data privacy and explainability are important.

AAAI Conference 2024 Conference Paper

Multi-Dimensional Fair Federated Learning

• Cong Su
• Guoxian Yu
• Jun Wang
• Hui Li
• Qingzhong Li
• Han Yu

Federated learning (FL) has emerged as a promising collaborative and secure paradigm for training a model from decentralized data without compromising privacy. Group fairness and client fairness are two dimensions of fairness that are important for FL. Standard FL can result in disproportionate disadvantages for certain clients, and it still faces the challenge of treating different groups equitably in a population. The problem of privately training fair FL models without compromising the generalization capability of disadvantaged clients remains open. In this paper, we propose a method, called mFairFL, to address this problem and achieve group fairness and client fairness simultaneously. mFairFL leverages differential multipliers to construct an optimization objective for empirical risk minimization with fairness constraints. Before aggregating locally trained models, it first detects conflicts among their gradients, and then iteratively curates the direction and magnitude of gradients to mitigate these conflicts. Theoretical analysis proves mFairFL facilitates the fairness in model development. The experimental evaluations based on three benchmark datasets show significant advantages of mFairFL compared to seven state-of-the-art baselines.

IJCAI Conference 2024 Conference Paper

Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection

• Xianjie Guo
• Kui Yu
• Hao Wang
• Lizhen Cui
• Han Yu
• Xiaoxiao Li

Federated causal discovery (FCD) aims to uncover causal relationships among variables from decentralized data across multiple clients, while preserving data privacy. In practice, the sample quality of each client's local data may vary across different variable spaces, referred to as sample quality heterogeneity. Thus, data from different clients might be suitable for learning different causal relationships among variables. Model aggregated under existing FCD methods requires the entire model parameters from each client, thereby being unable to handle the sample quality heterogeneity issue. In this paper, we propose the Federated Adaptive Causal Discovery (FedACD) method to bridge this gap. During federated model aggregation, it adaptively selects the causal relationships learned under the "good" variable space (i. e. , one with high-quality samples) from each client, while masking those learned under the "bad" variable space (i. e. , one with low-quality samples). This way, each client only needs to send the optimal learning results to the server, achieving accurate FCD. Extensive experiments on various types of datasets demonstrate significant advantages of FedACD over existing methods. The source code is available at https: //github. com/Xianjie-Guo/FedACD.

IJCAI Conference 2024 Conference Paper

Temporal Knowledge Graph Extrapolation via Causal Subhistory Identification

• Kai Chen
• Ye Wang
• Xin Song
• Siwei Chen
• Han Yu
• Aiping Li

Temporal knowledge graph extrapolation has become a prominent area of study interest in recent years. Numerous methods for extrapolation have been put forth, mining query-relevant information from history to generate forecasts. However, existing approaches normally do not discriminate between causal and non-causal effects in reasoning; instead, they focus on analyzing the statistical correlation between the future events to be predicted and the historical data given, which may be deceptive and hinder the model's capacity to learn real causal information that actually affects the reasoning conclusions. To tackle it, we propose a novel approach called Causal Subhistory Identification (CSI), which focuses on extracting the causal subhistory for reasoning purposes from a large amount of historical data. CSI can improve the clarity and transparency of the reasoning process and more effectively convey the logic behind conclusions by giving priority to the causal subhistory and eliminating non-causal correlations. Extensive experiments demonstrate the remarkable potential of our CSI in the following aspects: superiority, improvement, explainability, and robustness.

IJCAI Conference 2023 Conference Paper

Competitive-Cooperative Multi-Agent Reinforcement Learning for Auction-based Federated Learning

• Xiaoli Tang
• Han Yu

Auction-based Federated Learning (AFL) enables open collaboration among self-interested data consumers and data owners. Existing AFL approaches cannot manage the mutual influence among multiple data consumers competing to enlist data owners. Moreover, they cannot support a single data owner to join multiple data consumers simultaneously. To bridge these gaps, we propose the Multi-Agent Reinforcement Learning for AFL (MARL-AFL) approach to steer data consumers to bid strategically towards an equilibrium with desirable overall system characteristics. We design a temperature-based reward reassignment scheme to make tradeoffs between cooperation and competition among AFL data consumers. In this way, it can reach an equilibrium state that ensures individual data consumers can achieve good utility, while preserving system-level social welfare. To circumvent potential collusion behaviors among data consumers, we introduce a bar agent to set a personalized bidding lower bound for each data consumer. Extensive experiments on six commonly adopted benchmark datasets show that MARL-AFL is significantly more advantageous compared to six state-of-the-art approaches, outperforming the best by 12. 2%, 1. 9% and 3. 4% in terms of social welfare, revenue and accuracy, respectively.

IJCAI Conference 2023 Conference Paper

FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning

• Yuanyuan Chen
• Zichen Chen
• Pengcheng Wu
• Han Yu

Large-scale neural networks possess considerable expressive power. They are well-suited for complex learning tasks in industrial applications. However, large-scale models pose significant challenges for training under the current Federated Learning (FL) paradigm. Existing approaches for efficient FL training often leverage model parameter dropout. However, manipulating individual model parameters is not only inefficient in meaningfully reducing the communication overhead when training large-scale FL models, but may also be detrimental to the scaling efforts and model performance as shown by recent research. To address these issues, we propose the Federated Opportunistic Block Dropout (FedOBD) approach. The key novelty is that it decomposes large-scale models into semantic blocks so that FL participants can opportunistically upload quantized blocks, which are deemed to be significant towards training the model, to the FL server for aggregation. Extensive experiments evaluating FedOBD against four state-of-the-art approaches based on multiple real-world datasets show that it reduces the overall communication overhead by more than 88% compared to the best performing baseline approach, while achieving the highest test accuracy. To the best of our knowledge, FedOBD is the first approach to perform dropout on FL models at the block level rather than at the individual parameter level.

NeurIPS Conference 2023 Conference Paper

Recurrent Temporal Revision Graph Networks

• Yizhou Chen
• Anxiang Zeng
• Qingtao Yu
• Kerui Zhang
• Cao Yuanpeng
• Kangle Wu
• Guangda Huzhang
• Han Yu

Temporal graphs offer more accurate modeling of many real-world scenarios than static graphs. However, neighbor aggregation, a critical building block of graph networks, for temporal graphs, is currently straightforwardly extended from that of static graphs. It can be computationally expensive when involving all historical neighbors during such aggregation. In practice, typically only a subset of the most recent neighbors are involved. However, such subsampling leads to incomplete and biased neighbor information. To address this limitation, we propose a novel framework for temporal neighbor aggregation that uses the recurrent neural network with node-wise hidden states to integrate information from all historical neighbors for each node to acquire the complete neighbor information. We demonstrate the superior theoretical expressiveness of the proposed framework as well as its state-of-the-art performance in real-world applications. Notably, it achieves a significant +9. 4% improvement on averaged precision in a real-world Ecommerce dataset over existing methods on 2-layer models.

AAAI Conference 2023 Conference Paper

Stable Learning via Sparse Variable Independence

• Han Yu
• Peng Cui
• Yue He
• Zheyan Shen
• Yong Lin
• Renzhe Xu
• Xingxuan Zhang

The problem of covariate-shift generalization has attracted intensive research attention. Previous stable learning algorithms employ sample reweighting schemes to decorrelate the covariates when there is no explicit domain information about training data. However, with finite samples, it is difficult to achieve the desirable weights that ensure perfect independence to get rid of the unstable variables. Besides, decorrelating within stable variables may bring about high variance of learned models because of the over-reduced effective sample size. A tremendous sample size is required for these algorithms to work. In this paper, with theoretical justification, we propose SVI (Sparse Variable Independence) for the covariate-shift generalization problem. We introduce sparsity constraint to compensate for the imperfectness of sample reweighting under the finite-sample setting in previous methods. Furthermore, we organically combine independence-based sample reweighting and sparsity-based variable selection in an iterative way to avoid decorrelating within stable variables, increasing the effective sample size to alleviate variance inflation. Experiments on both synthetic and real-world datasets demonstrate the improvement of covariate-shift generalization performance brought by SVI.

AAAI Conference 2022 System Paper

CrowdFL: A Marketplace for Crowdsourced Federated Learning

• Daifei Feng
• Cicilia Helena
• Wei Yang Bryan Lim
• Jer Shyuan Ng
• Hongchao Jiang
• Zehui Xiong
• Jiawen Kang
• Han Yu

Amid data privacy concerns, Federated Learning (FL) has emerged as a promising machine learning paradigm that enables privacy-preserving collaborative model training. However, there exists a need for a platform that matches data owners (supply) with model requesters (demand). In this paper, we present CrowdFL, a platform to facilitate the crowdsourcing of FL model training. It coordinates client selection, model training, and reputation management, which are essential steps for the FL crowdsourcing operations. By implementing model training on actual mobile devices, we demonstrate that the platform improves model performance and training efficiency. To the best of our knowledge, it is the first platform to support crowdsourcing-based FL on edge devices.

AAAI Conference 2022 System Paper

Dynamic Incentive Mechanism Design for COVID-19 Social Distancing

• Xuan Rong Zane Ho
• Wei Yang Bryan Lim
• Hongchao Jiang
• Jer Shyuan Ng
• Han Yu
• Zehui Xiong
• Dusit Niyato
• Chunyan Miao

As countries enter the endemic phase of COVID-19, people’s risk of exposure to the virus is greater than ever. There is a need to make more informed decisions in our daily lives on avoiding crowded places. Crowd monitoring systems typically require costly infrastructure. We propose a crowdsourced crowd monitoring platform which leverages user inputs to generate crowd counts and forecast location crowdedness. A key challenge for crowd-sourcing is a lack of incentive for users to contribute. We propose a Reinforcement Learning based dynamic incentive mechanism to optimally allocate rewards to encourage user participation.

TIST Journal 2022 Journal Article

Federated Learning for Personalized Humor Recognition

• Xu Guo
• Han Yu
• Boyang Li
• Hao Wang
• Pengwei Xing
• Siwei Feng
• Zaiqing Nie
• Chunyan Miao

Computational understanding of humor is an important topic under creative language understanding and modeling. It can play a key role in complex human-AI interactions. The challenge here is that human perception of humorous content is highly subjective. The same joke may receive different funniness ratings from different readers. This makes it highly challenging for humor recognition models to achieve personalization in practical scenarios. Existing approaches are generally designed based on the assumption that users have a consensus on whether a given text is humorous or not. Thus, they cannot handle diverse humor preferences well. In this article, we propose the FedHumor approach for the recognition of humorous content in a personalized manner through Federated Learning (FL). Extending a pre-trained language model, FedHumor guides the fine-tuning process by considering diverse distributions of humor preferences from individuals. It incorporates a diversity adaptation strategy into the FL paradigm to train a personalized humor recognition model. To the best of our knowledge, FedHumor is the first text-based personalized humor recognition model through federated learning. Extensive experiments demonstrate the advantage of FedHumor in recognizing humorous texts compared to nine state-of-the-art humor recognition approaches with superior capability for handling the diversity in humor labels produced by users with diverse preferences.

TIST Journal 2022 Journal Article

GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning

• Zelei Liu
• Yuanyuan Chen
• Han Yu
• Yang Liu
• Lizhen Cui

Federated Learning (FL) bridges the gap between collaborative machine learning and preserving data privacy. To sustain the long-term operation of an FL ecosystem, it is important to attract high-quality data owners with appropriate incentive schemes. As an important building block of such incentive schemes, it is essential to fairly evaluate participants’ contribution to the performance of the final FL model without exposing their private data. Shapley Value (SV)–based techniques have been widely adopted to provide a fair evaluation of FL participant contributions. However, existing approaches incur significant computation costs, making them difficult to apply in practice. In this article, we propose the Guided Truncation Gradient Shapley (GTG-Shapley) approach to address this challenge. It reconstructs FL models from gradient updates for SV calculation instead of repeatedly training with different combinations of FL participants. In addition, we design a guided Monte Carlo sampling approach combined with within-round and between-round truncation to further reduce the number of model reconstructions and evaluations required. This is accomplished through extensive experiments under diverse realistic data distribution settings. The results demonstrate that GTG-Shapley can closely approximate actual Shapley values while significantly increasing computational efficiency compared with the state-of-the-art, especially under non-i.i.d. settings.

JBHI Journal 2022 Journal Article

NPI-RGCNAE: Fast Predicting ncRNA-Protein Interactions Using the Relational Graph Convolutional Network Auto-Encoder

• Han Yu
• Zi-Ang Shen
• Pu-Feng Du

ncRNAs play important roles in a variety of biological processes by interacting with RNA-binding proteins. Therefore, identifying ncRNA-protein interactions is important to understanding the biological functions of ncRNAs. Since experimental methods to determine ncRNA-protein interactions are always costly and time-consuming, computational methods have been proposed as alternative approaches. We developed a novel method NPI-RGCNAE (predicting ncRNA-Protein Interactions by the Relational Graph Convolutional Network Auto-Encoder). With a reliable negative sample selection strategy, we applied the Relational Graph Convolutional Network encoder and the DistMult decoder to predict ncRNA-protein interactions in an accurate and efficient way. By using the 5-fold cross-validation, we found that our method achieved a comparable performance to all state-of-the-art methods. Our method requires less than 10% training time of all state-of-the-art methods. It is a more efficient choice with large datasets in practice.

IJCAI Conference 2022 Conference Paper

Towards Verifiable Federated Learning

• Yanci Zhang
• Han Yu

Federated learning (FL) is an emerging paradigm of collaborative machine learning that preserves user privacy while building powerful models. Nevertheless, due to the nature of open participation by self-interested entities, it needs to guard against potential misbehaviours by legitimate FL participants. FL verification techniques are promising solutions for this problem. They have been shown to effectively enhance the reliability of FL networks and build trust among participants. Verifiable FL has become an emerging topic of research that has attracted significant interest from the academia and the industry alike. Currently, there is no comprehensive survey on the field of verifiable federated learning, which is interdisciplinary in nature and can be challenging for researchers to enter into. In this paper, we bridge this gap by reviewing works focusing on verifiable FL. We propose a novel taxonomy for verifiable FL covering both centralised and decentralised settings, summarise the commonly adopted performance evaluation approaches, and discuss promising directions towards a versatile verifiable FL framework.

AAAI Conference 2021 System Paper

AI-Empowered Decision Support for COVID-19 Social Distancing

• Hongchao Jiang
• Wei Yang Bryan Lim
• Jer Shyuan Ng
• Harold Ze Chie Teng
• Han Yu
• Zehui Xiong
• Dusit Niyato
• Chunyan Miao

The COVID-19 pandemic is one of the most severe challenges the world faces today. In order to contain the transmission of COVID-19, people around the world have been advised to practise social distancing. However, maintaining social distance is a challenging problem, as we often do not know beforehand how crowded the places we intend to visit are. In this paper, we demonstrate crowded. sg, an AIempowered platform that leverages on Unmanned Aerial Vehicles (UAVs), crowdsourced images, and computer vision techniques to provide social distancing decision support.

AAAI Conference 2021 Conference Paper

HyDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks

• Yuanyuan Chen
• Boyang Li
• Han Yu
• Pengcheng Wu
• Chunyan Miao

The behaviors of deep neural networks (DNNs) are notoriously resistant to human interpretations. In this paper, we propose Hypergradient Data Relevance Analysis, or HY- DRA, which interprets the predictions made by DNNs as effects of their training data. Existing approaches generally estimate data contributions around the final model parameters and ignore how the training data shape the optimization trajectory. By unrolling the hypergradient of test loss w. r. t. the weights of training data, HYDRA assesses the contribution of training data toward test data points throughout the training trajectory. In order to accelerate computation, we remove the Hessian from the calculation and prove that, under moderate conditions, the approximation error is bounded. Corroborating this theoretical claim, empirical results indicate the error is indeed small. In addition, we quantitatively demonstrate that HYDRA outperforms influence functions in accurately estimating data contribution and detecting noisy data labels. The source code is available at https: //github. com/cyyever/aaai hydra.

IJCAI Conference 2021 Conference Paper

Predictive Analytics for COVID-19 Social Distancing

• Harold Ze Chie Teng
• Hongchao Jiang
• Xuan Rong Zane Ho
• Wei Yang Bryan Lim
• Jer Shyuan Ng
• Han Yu
• Zehui Xiong
• Dusit Niyato

The COVID-19 pandemic has disrupted the lives of millions across the globe. In Singapore, promoting safe distancing by managing crowds in public areas have been the cornerstone of containing the community spread of the virus. One of the most important solutions to maintain social distancing is to monitor the crowdedness of indoor and outdoor points of interest. Using Nanyang Technological University (NTU) as a testbed, we develop and deploy a platform that provides live and predicted crowd counts for key locations on campus to help users plan their trips in an informed manner, so as to mitigate the risk of community transmission.

IJCAI Conference 2020 Conference Paper

A Multi-player Game for Studying Federated Learning Incentive Schemes

• Kang Loon Ng
• Zichen Chen
• Zelei Liu
• Han Yu
• Yang Liu
• Qiang Yang

Federated Learning (FL) enables participants to "share'' their sensitive local data in a privacy preserving manner and collaboratively build machine learning models. In order to sustain long-term participation by high quality data owners (especially if they are businesses), FL systems need to provide suitable incentives. To design an effective incentive scheme, it is important to understand how FL participants respond under such schemes. This paper proposes FedGame, a multi-player game to study how FL participants make action selection decisions under different incentive schemes. It allows human players to role-play under various conditions. The decision-making processes can be analyzed and visualized to inform FL incentive mechanism design in the future.

IS Journal 2020 Journal Article

A Sustainable Incentive Scheme for Federated Learning

• Han Yu
• Zelei Liu
• Yang Liu
• Tianjian Chen
• Mingshu Cong
• Xi Weng
• Dusit Niyato
• Qiang Yang

In federated learning (FL), a federation distributedly trains a collective machine learning model by leveraging privacy preserving technologies. However, FL participants need to incur some cost for contributing to the FL models. The training and commercialization of the models will take time. Thus, there will be delays before the federation could pay back the participants. This temporary mismatch between contributions and rewards has not been accounted for by existing payoff-sharing schemes. To address this limitation, we propose the FL incentivizer (FLI). It dynamically divides a given budget in a context-aware manner among data owners in a federation by jointly maximizing the collective utility while minimizing the inequality among the data owners, in terms of the payoff received and the waiting time for receiving payoffs. Comparisons with five state-of-the-art payoff-sharing schemes show that FLI attracts high-quality data owners and achieves the highest expected revenue for a federation.

IJCAI Conference 2020 Conference Paper

A Testbed for Studying COVID-19 Spreading in Ride-Sharing Systems

• Harrison Jun Yong Wong
• Zichao Deng
• Han Yu
• Jianqiang Huang
• Cyril Leung
• Chunyan Miao

Order dispatch is an important area where artificial intelligence (AI) can benefit ride-sharing systems (e. g. , Grab, Uber), which has become an integral part of our public transport network. In this paper, we present a multi-agent testbed to study the spread of infectious diseases through such a system. It allows users to vary the parameters of the disease and behaviours to study the interaction effect between technology, disease and people's behaviours in such a complex environment.

IJCAI Conference 2020 Conference Paper

An AI-empowered Visual Storyline Generator

• Chang Liu
• Zhao Yong Lim
• Han Yu
• Zhiqi Shen
• Ian Dixon
• Zhanning Gao
• Pan Wang
• Peiran Ren

Video editing is currently a highly skill- and time-intensive process. One of the most important tasks in video editing is to compose the visual storyline. This paper outlines Visual Storyline Generator (VSG), an artificial intelligence (AI)-empowered system that automatically generates visual storylines based on a set of images and video footages provided by the user. It is designed to produce engaging and persuasive promotional videos with an easy-to-use interface. In addition, users can be involved in refining the AI-generated visual storylines. The editing results can be used as training data to further improve the AI algorithms in VSG.

AAAI Conference 2020 Short Paper

Efficient Spatial-Temporal Rebalancing of Shareable Bikes (Student Abstract)

• Zichao Deng
• Anqi Tu
• Zelei Liu
• Han Yu

Bike sharing systems are popular worldwide now. However, these systems are facing a problem - rebalancing of shareable bikes among different docking stations. To address this challenge, we propose an approach for the spatial-temporal rebalancing of shareable bikes which allows domain experts to optimize the rebalancing operation with their knowledge and preferences without relying on learning by trial-and-error.

AAAI Conference 2020 Short Paper

Generating Engaging Promotional Videos for E-commerce Platforms (Student Abstract)

• Chang Liu
• Han Yu
• Yi Dong
• Zhiqi Shen
• Yingxue Yu
• Ian Dixon
• Zhanning Gao
• Pan Wang

There is an emerging trend for sellers to use videos to promote their products on e-commerce platforms such as Taobao. com. Current video production workflow includes the production of visual storyline by human directors. We propose a system to automatically generate visual storyline based on the input set of visual materials (e. g. video clips or still images) and then produce a promotional video. In particular, we propose an algorithm called Shot Composition, Selection and Plotting (ShotCSP), which generates visual storylines leveraging film-making principles to improve viewing experience and perceived persuasiveness.

IS Journal 2020 Journal Article

Introduction to the Special Issue on Federated Machine Learning

• Yang Liu
• Han Yu
• Qiang Yang

The articles in this special section focus on federated machine learning, an emerging research paradigm focusing on solving data-silos challenges in real-world industrial applications. It is a broad discipline that touches many topics, including distributed and collaborative learning, privacy-preserving machine learning, edge computing, and data valuation, etc. Its interdisciplinary nature calls for collaborative efforts from a variety of fields to establish new protocols, frameworks and systems to address unique challenges, and open problems. These articles highlight a selection of high-quality and original works in this new area, including accepted papers to the 1st International Workshop on Federated Machine Learning in conjunction with IJCAI 2019.

TIST Journal 2020 Journal Article

Transfer Learning with Dynamic Distribution Adaptation

• Jindong Wang
• Yiqiang Chen
• Wenjie Feng
• Han Yu
• Meiyu Huang
• Qiang Yang

Transfer learning aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions. However, in real applications, the marginal and conditional distributions usually have different contributions to the domain discrepancy. Existing methods fail to quantitatively evaluate the different importance of these two distributions, which will result in unsatisfactory transfer performance. In this article, we propose a novel concept called Dynamic Distribution Adaptation (DDA), which is capable of quantitatively evaluating the relative importance of each distribution. DDA can be easily incorporated into the framework of structural risk minimization to solve transfer learning problems. On the basis of DDA, we propose two novel learning algorithms: (1) Manifold Dynamic Distribution Adaptation (MDDA) for traditional transfer learning, and (2) Dynamic Distribution Adaptation Network (DDAN) for deep transfer learning. Extensive experiments demonstrate that MDDA and DDAN significantly improve the transfer learning performance and set up a strong baseline over the latest deep and adversarial methods on digits recognition, sentiment analysis, and image classification. More importantly, it is shown that marginal and conditional distributions have different contributions to the domain divergence, and our DDA is able to provide good quantitative evaluation of their relative importance, which leads to better performance. We believe this observation can be helpful for future research in transfer learning.

TIST Journal 2019 Journal Article

A Survey of Zero-Shot Learning

• Wei Wang
• Vincent W. Zheng
• Han Yu
• Chunyan Miao

Most machine-learning methods focus on classifying instances whose classes have already been seen in training. In practice, many applications require classifying instances whose classes have not been seen previously. Zero-shot learning is a powerful and promising learning paradigm, in which the classes covered by training instances and the classes we aim to classify are disjoint. In this paper, we provide a comprehensive survey of zero-shot learning. First of all, we provide an overview of zero-shot learning. According to the data utilized in model optimization, we classify zero-shot learning into three learning settings. Second, we describe different semantic spaces adopted in existing zero-shot learning works. Third, we categorize existing zero-shot learning methods and introduce representative methods under each category. Fourth, we discuss different applications of zero-shot learning. Finally, we highlight promising future research directions of zero-shot learning.

IJCAI Conference 2019 Conference Paper

Agent-based Decision Support for Pain Management in Primary Care Settings

• Xu Guo
• Han Yu
• Chunyan Miao
• Yiqiang Chen

The lack of systematic pain management training and support among primary care physicians (PCPs) limits their ability to provide quality care for patients with pain. Here, we demonstrate an Agent-based Clinical Decision Support System to empower PCPs to leverage knowledge from pain specialists. The system learns a general-purpose representation space on patients, automatically diagnoses pain, recommends therapy and medicine, and suggests a referral program to PCPs in their decision-making tasks.

IJCAI Conference 2019 Conference Paper

An Online Intelligent Visual Interaction System

• Anxiang Zeng
• Han Yu
• Xin Gao
• Kairi Ou
• Zhenchuan Huang
• Peng Hou
• Mingli Song
• Jingshu Zhang

This paper proposes an Online Intelligent Visual Interactive System (OIVIS), which can be applied to various live video broadcast and short video scenes to provide an interactive user experience. In the live video broadcast, the anchor can issue various commands by using pre-defined gestures, and can trigger real-time background replacement to create an immersive atmosphere. To support such dynamic interactivity, we implemented algorithms including real-time gesture recognition and real-time video portrait segmentation, developed a deep network inference framework, and a real-time rendering framework AI Gender at the front end to create a complete set of visual interaction solutions for use in resource constrained mobile.

AAAI Conference 2019 Short Paper

Ethically Aligned Mobilization of Community Effort to Reposition Shared Bikes

• Zelei Liu
• Han Yu
• Leye Wang
• Liang Hu
• Qiang Yang

We consider the problem of mobilizing community effort to reposition indiscriminantly parked shared bikes in urban environments through crowdsourcing. We propose an ethically aligned incentive optimization approach WSLS which maximizes the rate of success for bike repositioning while minimizing cost and prioritizing users’ wellbeing. Realistic simulations based on a dataset from Singapore demonstrate that WSLS significantly outperforms existing approaches.

AAMAS Conference 2019 Conference Paper

Ethically Aligned Multi-agent Coordination to Enhance Social Welfare

• Han Yu
• Zhiqi Shen
• Lizhen Cui
• Yongqing Zheng
• Victor R. Lesser

In multi-agent systems (MASs), the complex interactions among self-interested agents can be modelled as stochastic games. Existing decision support approaches dealing with such situations focus on minimizing individual agent’s regret through outperforming other agents in the competitive aspect of the game. Such an approach often results in social welfare not being maximized in the process. In this paper, we propose the regret-minimization-social-welfare-maximization (RMSM) approach. It contains a novel method to quantify how an agent’s sacrifice increases and decreases over time based on queueing system dynamics. In this way, ensuring fairness of distribution of sacrifice among agents and compensating for their previous sacrifices can be translated into maintaining the stability of a queueing system.

IJCAI Conference 2019 Conference Paper

Fair and Explainable Dynamic Engagement of Crowd Workers

• Han Yu
• Yang Liu
• Xiguang Wei
• Chuyu Zheng
• Tianjian Chen
• Qiang Yang
• Xiong Peng

Years of rural-urban migration has resulted in a significant population in China seeking ad-hoc work in large urban centres. At the same time, many businesses face large fluctuations in demand for manpower and require more efficient ways to satisfy such demands. This paper outlines AlgoCrowd, an artificial intelligence (AI)-empowered algorithmic crowdsourcing platform. Equipped with an efficient explainable task-worker matching optimization approach designed to focus on fair treatment of workers while maximizing collective utility, the platform provides explainable task recommendations to workers' personal work management mobile apps which are becoming popular, with the aim to address the above societal challenge.

IJCAI Conference 2019 Conference Paper

Intelligent Decision Support for Improving Power Management

• Yongqing Zheng
• Han Yu
• Kun Zhang
• Yuliang Shi
• Cyril Leung
• Chunyan Miao

With the development and adoption of the electricity information tracking system in China, real-time electricity consumption big data have become available to enable artificial intelligence (AI) to help power companies and the urban management departments to make demand side management decisions. We demonstrate the Power Intelligent Decision Support (PIDS) platform, which can generate Orderly Power Utilization (OPU) decision recommendations and perform Demand Response (DR) implementation management based on a short-term load forecasting model. It can also provide different users with query and application functions to facilitate explainable decision support.

IJCAI Conference 2019 Conference Paper

Multi-Agent Visualization for Explaining Federated Learning

• Xiguang Wei
• Quan Li
• Yang Liu
• Han Yu
• Tianjian Chen
• Qiang Yang

As an alternative decentralized training approach, Federated Learning enables distributed agents to collaboratively learn a machine learning model while keeping personal/private information on local devices. However, one significant issue of this framework is the lack of transparency, thus obscuring understanding of the working mechanism of Federated Learning systems. This paper proposes a multi-agent visualization system that illustrates what is Federated Learning and how it supports multi-agents coordination. To be specific, it allows users to participate in the Federated Learning empowered multi-agent coordination. The input and output of Federated Learning are visualized simultaneously, which provides an intuitive explanation of Federated Learning for users in order to help them gain deeper understanding of the technology.

AAMAS Conference 2019 Conference Paper

Social Mobilization to Reposition Indiscriminately Parked Shareable Bikes

• Zelei Liu
• Han Yu
• Leye Wang
• Liang Hu
• Qiang Yang

With rapid growth of shareable bikes comes the problem of indiscriminately parked bikes blocking traffic. We propose a centralized pricing based dynamic incentive mechanism to mobilize the participants via crowdsourcing with regarding to reposition the indiscriminately parked bikes. We formalize the key component of the proposed incentive mechanism into two decision-making model: individual decision-making model Cost-refundable, Multiple Resources Constrained Multiple Armed Bandit (CRMR-MAB) and overall decision-making model multi-dimensional and multiple choice Knapsack problem (MMKP). We proposed a comprehensive decision algorithm GA-WSLS which combines the two. Realistic simulation based on real-world dataset from Singapore demonstrated significant advantages of the proposed approach over 7 existing approaches.

IJCAI Conference 2018 Conference Paper

Building Ethics into Artificial Intelligence

• Han Yu
• Zhiqi Shen
• Chunyan Miao
• Cyril Leung
• Victor R. Lesser
• Qiang Yang

As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination. Within the AI research community, this topic remains less familiar to many researchers. In this paper, we complement existing surveys, which largely focused on the psychological, social and legal discussions of the topic, with an analysis of recent advances in technical solutions for AI governance. By reviewing publications in leading AI conferences including AAAI, AAMAS, ECAI and IJCAI, we propose a taxonomy which divides the field into four areas: 1) exploring ethical dilemmas; 2) individual ethical decision frameworks; 3) collective ethical decision frameworks; and 4) ethics in human-AI interactions. We highlight the intuitions and key techniques used in each approach, and discuss promising future research directions towards successful integration of ethical AI systems into human societies.

AAAI Conference 2018 Conference Paper

SmartHS: An AI Platform for Improving Government Service Provision

• Yongqing Zheng
• Han Yu
• Lizhen Cui
• Chunyan Miao
• Cyril Leung
• Qiang Yang

Over the years, government service provision in China has been plagued by inefficiencies. Previous attempts to address this challenge following a toolbox e-government system model in China were not effective. In this paper, we report on a successful experience in improving government service provision in the domain of social insurance in Shandong Province, China. Through standardization of service work- flows following the Complete Contract Theory (CCT) and the infusion of an artificial intelligence (AI) engine to maximize the expected quality of service while reducing waiting time, the Smart Human-resource Services (SmartHS) platform transcends organizational boundaries and improves system efficiency. Deployments in 3 cities involving 2, 000 participating civil servants and close to 3 million social insurance service cases over a 1 year period demonstrated that SmartHS significantly improves user experience with roughly a third of the original front desk staff. This new AI-enhanced mode of operation is useful for informing current policy discussions in many domains of government service provision.

AAAI Conference 2017 Short Paper

A Computational Assessment Model for the Adaptive Level of Rehabilitation Exergames for the Elderly

• Hao Zhang
• Chunyan Miao
• Han Yu
• Cyril Leung

Rehabilitation exergames can engage the elderly in physical activities and help them recover part of their deteriorating capabilities. However, most existing exergames lack measures of how suitable they are to specific individuals. In this paper, we propose the Computational Person-Environment Fit model to evaluate the adaptability of the exergames to each individual elderly user.

TIST Journal 2017 Journal Article

Adult Image and Video Recognition by a Deep Multicontext Network and Fine-to-Coarse Strategy

• Xinyu Ou
• Hefei Ling
• Han Yu
• Ping Li
• Fuhao Zou
• Si Liu

Adult image and video recognition is an important and challenging problem in the real world. Low-level feature cues do not produce good enough information, especially when the dataset is very large and has various data distributions. This issue raises a serious problem for conventional approaches. In this article, we tackle this problem by proposing a deep multicontext network with fine-to-coarse strategy for adult image and video recognition. We employ a deep convolution networks to model fusion features of sensitive objects in images. Global contexts and local contexts are both taken into consideration and are jointly modeled in a unified multicontext deep learning framework. To make the model more discriminative for diverse target objects, we investigate a novel hierarchical method, and a task-specific fine-to-coarse strategy is designed to make the multicontext modeling more suitable for adult object recognition. Furthermore, some recently proposed deep models are investigated. Our approach is extensively evaluated on four different datasets. One dataset is used for ablation experiments, whereas others are used for generalization experiments. Results show significant and consistent improvements over the state-of-the-art methods.

AAAI Conference 2016 Conference Paper

A Fraud Resilient Medical Insurance Claim System

• Yuliang Shi
• Chenfei Sun
• Qingzhong Li
• Lizhen Cui
• Han Yu
• Chunyan Miao

As many countries in the world start to experience population aging, there are an increasing number of people relying on medical insurance to access healthcare resources. Medical insurance frauds are causing billions of dollars in losses for public healthcare funds. The detection of medical insurance frauds is an important and difficult challenge for the artificial intelligence (AI) research community. This paper outlines HFDA, a hybrid AI approach to effectively and efficiently identify fraudulent medical insurance claims which has been tested in an online medical insurance claim system in China.

AAMAS Conference 2016 Conference Paper

A Hybrid Approach for Detecting Fraudulent Medical Insurance Claims (Extended Abstract)

• Chenfei Sun
• Yuliang Shi
• Qingzhong Li
• Lizhen Cui
• Han Yu
• Chunyan Miao

Medical insurance frauds are causing huge losses for public healthcare funds in many countries. Detecting medical insurance frauds is an important and difficult challenge. Because of the complex granularity of data, existing fraud detection approaches tend to be less effective in terms of recalling fraudulent claim behaviours. In this paper, we propose a Hybrid Fraud Detection Approach (HFDA) to address this problem, which is compared with four state-of-the-art approaches using a real-world dataset. Extensive experiment results show that the proposed approach is significantly more effective and efficient.

AAMAS Conference 2016 Conference Paper

A Kinect-based Interactive Game to Improve the Cognitive Inhibition of the Elderly (Demonstration)

• Siyuan Liu
• Zhiqi Shen
• Han Yu
• Han Lin
• Zhengjin Guo
• Zhengxiang Pan
• Chunyan Miao
• Cryil Leung

Cognitive abilities, including cognitive inhibition, degenerate with the aging process. In this demonstration, we present a Kinect-based interactive game which aims to improve the cognitive inhibition ability of the elderly. The game is designed in the table tennis theme, and the adoption of Kinect makes it convenient for the elderly to use. The players’ in-game behaviour data are recorded for the health advisor agent to conduct personalization, analysis, and decision making. A pilot study has been conducted to investigate the relationship between the players’ cognitive inhibition abilities and their in-game performance. The study results suggest that the in-game performance can reflect a player’s cognitive inhibition ability, and indicate that the game can be used to improve the cognitive inhibition ability of the elderly in the future.

AAAI Conference 2016 Conference Paper

Efficient Collaborative Crowdsourcing

• Zhengxiang Pan
• Han Yu
• Chunyan Miao
• Cyril Leung

We consider the problem of making efficient quality-timecost trade-offs in collaborative crowdsourcing systems in which different skills from multiple workers need to be combined to complete a task. We propose CrowdAsm - an approach which helps collaborative crowdsourcing systems determine how to combine the expertise of available workers to maximize the expected quality of results while minimizing the expected delays. Analysis proves that CrowdAsm can achieve close to optimal profit for workers in a given crowdsourcing system if they follow the recommendations.

AAAI Conference 2016 Conference Paper

Multi-Agent System Development MADE Easy

• Zhiqi Shen
• Han Yu
• Chunyan Miao
• Siyao Li
• Yiqiang Chen

Agent-Oriented Software Engineering (AOSE) is an emerging software engineering paradigm that advocates the application of best practices in the development of Multi-Agent Systems (MAS) through the use of agents and organizations of agents. This paper outlines the MADE system, which provides an interactive platform for people who are not wellversed in AOSE to contribute to the rapid prototyping of MASs with ease.

AAAI Conference 2016 Conference Paper

Productive Aging through Intelligent Personalized Crowdsourcing

• Han Yu
• Chunyan Miao
• Siyuan Liu
• Zhengxiang Pan
• Nur Syahidah Khalid
• Zhiqi Shen
• Cyril Leung

The current generation of senior citizens are enjoying unparalleled levels of good health than previous generations. The need for personal fulfilment after retirement has driven many of them to participate in productive aging activities such as volunteering. This paper outlines the Silver Productive (SP) mobile app, a system powered by the RTS-P intelligent personalized task sub-delegation approach with dynamic worker effort pricing functions. It provides an algorithmic crowdsourcing platform to enable seniors to contribute their effort through productive aging activities and help organizations ef- ficiently utilize seniors’ collective productivity.

AAAI Conference 2015 Conference Paper

Efficient Task Sub-Delegation for Crowdsourcing

• Han Yu
• Chunyan Miao
• Zhiqi Shen
• Cyril Leung
• Yiqiang Chen
• Qiang Yang

Reputation-based approaches allow a crowdsourcing system to identify reliable workers to whom tasks can be delegated. In crowdsourcing systems that can be modeled as multi-agent trust networks consist of resource constrained trustee agents (i. e. , workers), workers may need to further sub-delegate tasks to others if they determine that they cannot complete all pending tasks before the stipulated deadlines. Existing reputation-based decision-making models cannot help workers decide when and to whom to sub-delegate tasks. In this paper, we proposed a reputation aware task sub-delegation (RTS) approach to bridge this gap. By jointly considering a worker’s reputation, workload, the price of its effort and its trust relationships with others, RTS can be implemented as an intelligent agent to help workers make sub-delegation decisions in a distributed manner. The resulting task allocation maximizes social welfare through efficient utilization of the collective capacity of a crowd, and provides provable performance guarantees. Experimental comparisons with state-of-the-art approaches based on the Epinions trust network demonstrate significant advantages of RTS under high workload conditions.

AAAI Conference 2014 Conference Paper

RepRev: Mitigating the Negative Effects of Misreported Ratings

• Yuan Liu
• Siyuan Liu
• Jie Zhang
• Hui Fang
• Han Yu
• Chunyan Miao

Reputation models depend on the ratings provided by buyers to gauge the reliability of sellers in multi-agent based e-commerce environment. However, there is no prevention for the cases in which a buyer misjudges a seller, and provides a negative rating to an original satisfactory transaction. In this case, how should the seller get his reputation repaired and utility loss recovered? In this work, we propose a mechanism to mitigate the negative effect of the misreported ratings. It temporarily inflates the reputation of the victim seller with a certain value for a period of time. This allows the seller to recover his utility loss due to lost opportunities caused by the misreported ratings. Experiments demonstrate the necessity and effectiveness of the proposed mechanism.

AAAI Conference 2014 Conference Paper

Reputation-Aware Continuous Double Auction

• Yuan Liu
• Jie Zhang
• Han Yu
• Chunyan Miao

Truthful bidding is a desirable property for continuous double auctions (CDAs). Many incentive mechanisms have been proposed to elicit truthful bids. However, existing truthful CDA mechanisms often overlook the possibility that sellers may choose not to deliver the auctioned items to buyers as promised. In this situation, buyers may become unwilling to bid their true valuations in the future to compensate for their risks of being cheated, thereby rendering CDAs ineffective. In this paper, we propose a novel reputation-aware CDA (named RCDA) mechanism to consider the honesty of auction participants. It dynamically adjusts bids and asks according to the reputation of participants to reflect the risks involved in the transactions. Theoretical analysis proves that RCDA is effective in eliciting truthful bids from buyers and sellers in the presence of possible dishonest behavior from both buyers and sellers.

IJCAI Conference 2013 Conference Paper

A Reputation Management Approach for Resource Constrained Trustee Agents

• Han Yu
• Chunyan Miao
• Bo An
• Cyril Leung
• Victor R. Lesser

Trust is an important mechanism enabling agents to self-police open and dynamic multi-agent systems (ODMASs). Trusters evaluate the reputation of trustees based on their past observed performance, and use this information to guide their future interaction decisions. Existing trust models tend to concentrate trusters’ interactions on a small number of highly reputable trustees to minimize risk exposure. When a trustee’s servicing capacity is limited, such an approach may cause long delays for trusters and subsequently damage the reputation of trustees. To mitigate this problem, we propose a reputation management approach for trustee agents based on distributed constraint optimization. It helps a trustee to make situation-aware decisions on which incoming requests to serve and prevent the resulting reputation score from being affected by factors out of the trustee’s control. The approach is evaluated through theoretical analysis and within a simulated, highly dynamic multi-agent environment. The results show that it can achieve close to optimally efficient utilization of the trustee agents’ collective capacity in an ODMAS, promotes fair treatment of trustee agents based on their behavior, and significantly outperforms related work in enhancing social welfare.

AAMAS Conference 2013 Conference Paper

A Reputation-aware Decision-making Approach for Improving the Efficiency of Crowdsourcing Systems

• Han Yu
• Zhiqi Shen
• Chunyan Miao
• Bo An

A crowdsourcing system is a useful platform for utilizing the intelligence and skills of the mass. Nevertheless, like any open system that involves the exchange of things of value, selfish and malicious behaviors exist in crowdsourcing systems and need to be mitigated. Trust management has been proven to be a viable solution in many systems. However, a major difference between crowdsourcing systems and existing trust models designed for multi-agent systems is that human trustees have limited task processing capacity per unit time compared to an intelligent agent program. This paper recognizes a problem in current trust-aware decision-making methods for task assignment in crowdsourcing platforms. On the one hand, trust-based methods over-assign tasks to trusted workers, while on the other hand, workload-based solutions do not give sufficient guarantees on the quality of work. The proposed solution, the social welfare optimizing reputation-aware decision-making (SWORD) approach, strikes a balance between the two and is shown through extensive simulations to significantly improve social welfare of crowdsourcing platforms compared to related work.

AAMAS Conference 2013 Conference Paper

The Innovative Application of Learning Companions in Virtual Singapura

• Qiong Wu
• Xiaogang Han
• Han Yu
• Zhiqi Shen
• Chunyan Miao

Virtual Singapura (VS) is a virtual world based learning environment designed to facilitate learning of the plant transport system. During field studies of VS, we observed that students in virtual world tend to be attracted by visual and auditorial stimuli and be distracted from learning objectives. Also, intensive cognitive load can affect students’ learning experience. To address these issues, we propose two types of companion agent, namely curious companion and remembrance companion. Results collected from the field studies indicate advantages of learning companion augmented virtual world in enhancing students’ learning experience.

AAMAS Conference 2011 Conference Paper

A Simple Curious Agent to Help People be Curious

• Han Yu
• Zhiqi Shen
• Chunyan Miao
• Ah-Hwee Tan

Curiosity is an innately rewarding state of mind that, over the millennia, has driven the human race to explore and discover. Many researches in pedagogical science have confirmed the importance of being curious to the students' cognitive development. However, in the newly popular virtual world-based learning environments (VLEs), there is currently a lack of attention being paid to enhancing the learning experience by stimulating the learners' curiosity. In this paper, we propose a simple model for curious agents (CAs) which can be used to stimulate learners' curiosity in VLEs. Potential future research directions will be discussed.