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Yidong Li

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14 papers

2 author rows

AAAI Conference 2026 Conference Paper

Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach

Jundong Chen
Honglei Zhang
Chunxu Zhang
Fangyuan Luo
Yidong Li

Federated recommendation (FR) facilitates collaborative training by aggregating local models from massive devices, enabling client-specific personalization while ensuring privacy. However, we empirically and theoretically demonstrate that server-side aggregation can undermine client-side personalization, leading to suboptimal performance, i.e., the aggregation bottleneck. This issue stems from the inherent heterogeneity across numerous clients in FR, which drives the global model to deviate from local optima. To this end, we propose FedEM, which elastically merges the global and local models to compensate for impaired personalization. Unlike existing personalized federated recommendation (pFR) methods, FedEM (1) investigates the aggregation bottleneck in FR through theoretical insights, rather than relying on heuristic analysis; (2) leverages off-the-shelf local models rather than designing additional mechanisms to boost personalization. Extensive experiments demonstrate that our method preserves client personalization during collaborative training, outperforming state-of-the-art baselines.

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AAAI Conference 2026 Conference Paper

TransFR: Transferable Federated Recommendation with Adapter Tuning on Pre-trained Language Models

Honglei Zhang
Zhiwei Li
Haoxuan Li
Xin Zhou
Jie Zhang
Yidong Li

Federated recommendations (FRs), facilitating multiple local clients to collectively learn a global model without disclosing user private data, have emerged as a prevalent on-device service. In conventional FRs, a dominant paradigm is to utilize discrete identities to represent clients and items, which are then mapped to domain-specific embeddings to participate in model training. Despite considerable performance, we reveal three inherent limitations that can not be ignored in federated settings, i.e., non-transferability across domains, ineffectiveness in cold-start settings, and potential privacy violations during federated training. To this end, we propose a transferable federated recommendation model, TransFR, which delicately incorporates the general capabilities empowered by pre-trained models and the personalized abilities by fine-tuning local private data. Specifically, it first learns domain-agnostic representations of items by exploiting pre-trained models with public textual corpora. To tailor for FR tasks, we further introduce efficient federated adapter-tuning and post-adaptation personalization, which facilitate personalized adapters for each client by fitting local private data. We theoretically prove the advantages of incorporating adapter tuning in FRs regarding both effectiveness and privacy. Through extensive experiments, we show that our TransFR surpasses state-of-the-art FRs on transferability.

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EAAI Journal 2025 Journal Article

An adaptive weighted stacking ensemble framework for photovoltaic power generation forecasting with joint optimization of features and hyperparameters

Shaolong Zheng
Danyun Li
Yidong Li
Zihan Zeng
Yuxuan Zhou
Changsi Li
Kun Song

Accurate photovoltaic (PV) power forecasting is crucial for the efficient operation of power systems. However, the limited availability of historical data often hinders prediction accuracy. To address this challenge, an adaptive weighted stacking (AWS) ensemble framework is proposed, combining artificial intelligence ensemble models with joint optimization (JO) of features and hyperparameters to enhance prediction performance in small sample (SS) scenarios. In the framework, a base learner selection strategy based on kernel hierarchical clustering (KHC) is used to balance model performance and diversity. Multi-initiated parallel bayesian optimization with early stopping (MI-PBOES) algorithm is employed for the JO of features and hyperparameters to obtain the optimal subset of features and the corresponding parameter configuration. Additionally, an AWS strategy is employed to dynamically adjust ensemble weights, further improving accuracy and robustness. To evaluate its effectiveness in engineering applications, experiments were conducted using data from the desert knowledge australia (DKA) solar centre. The results indicate that the proposed framework reduces root mean square error (RMSE) by 51. 84% and mean absolute error (MAE) by 56. 42% compared to traditional machine learning models, while RMSE decreases by 129. 89% and MAE by 124. 82% compared to deep learning models. Under noisy conditions, RMSE and MAE are reduced by 39. 46% and 37. 37%, respectively, showing remarkable prediction performance and robustness.

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AAAI Conference 2025 Conference Paper

CoDTS: Enhancing Sparsely Supervised Collaborative Perception with a Dual Teacher-Student Framework

Yushan Han
Hui Zhang
Honglei Zhang
Jing Wang
Yidong Li

Current collaborative perception methods often rely on fully annotated datasets, which can be expensive to obtain in practical situations. To reduce annotation costs, some works adopt sparsely supervised learning techniques and generate pseudo labels for the missing instances. However, these methods fail to achieve an optimal confidence threshold that harmonizes the quality and quantity of pseudo labels. To address this issue, we propose an end-to-end Collaborative perception Dual Teacher-Student framework (CoDTS), which employs adaptive complementary learning to produce both high-quality and high-quantity pseudo labels. Specifically, the Main Foreground Mining (MFM) module generates high-quality pseudo labels based on the prediction of the static teacher. Subsequently, the Supplement Foreground Mining (SFM) module ensures a balance between the quality and quantity of pseudo labels by adaptively identifying missing instances based on the prediction of the dynamic teacher. Additionally, the Neighbor Anchor Sampling (NAS) module is incorporated to enhance the representation of pseudo labels. To promote the adaptive complementary learning, we implement a staged training strategy that trains the student and dynamic teacher in a mutually beneficial manner. Extensive experiments demonstrate that the CoDTS effectively ensures an optimal balance of pseudo labels in both quality and quantity, establishing a new state-of-the-art in sparsely supervised collaborative perception.

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NeurIPS Conference 2025 Conference Paper

Neural Fractional Attention Differential Equations

Qiyu Kang
Wenjun Cui
Xuhao Li
Yuxin Ma
Xueyang Fu
Wee Peng Tay
Yidong Li
Zheng-Jun Zha

The integration of differential equations with neural networks has created powerful tools for modeling complex dynamics effectively across diverse machine learning applications. While standard integer-order neural ordinary differential equations (ODEs) have shown considerable success, they are limited in their capacity to model systems with memory effects and historical dependencies. Fractional calculus offers a mathematical framework capable of addressing this limitation, yet most current fractional neural networks use static memory weightings that cannot adapt to input-specific contextual requirements. This paper proposes a generalized neural Fractional Attention Differential Equation (FADE), which combines the memory-retention capabilities of fractional calculus with contextual learnable attention mechanisms. Our approach replaces fixed kernel functions in fractional operators with neural attention kernels that adaptively weight historical states based on their contextual relevance to current predictions. This allows our framework to selectively emphasize important temporal dependencies while filtering less relevant historical information. Our theoretical analysis establishes solution boundedness, problem well-posedness, and numerical equation solver convergence properties of the proposed model. Furthermore, through extensive evaluation on tasks such as fluid flow, graph learning problems and spatio-temporal traffic flow forecasting, we demonstrate that our adaptive attention-based fractional framework outperforms both integer-order neural ODE models and existing fractional approaches. The results confirm that our framework provides superior modeling capacity for complex dynamics with varying temporal dependencies. The code is available at \url{https: //github. com/cuiwjTech/NeurIPS2025_FADE}.

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AAAI Conference 2025 Conference Paper

Neural Variable-Order Fractional Differential Equation Networks

Wenjun Cui
Qiyu Kang
Xuhao Li
Kai Zhao
Wee Peng Tay
Weihua Deng
Yidong Li

The use of neural differential equation models in machine learning applications has gained significant traction in recent years. In particular, fractional differential equations (FDEs) have emerged as a powerful tool for capturing complex dynamics in various domains. While existing models have primarily focused on constant-order fractional derivatives, variable-order fractional operators offer a more flexible and expressive framework for modeling complex memory patterns. In this work, we introduce the Neural Variable-Order Fractional Differential Equation network (NvoFDE), a novel neural network framework that integrates variable-order fractional derivatives with learnable neural networks. Our framework allows for the modeling of adaptive derivative orders dependent on hidden features, capturing more complex feature-updating dynamics and providing enhanced flexibility. We conduct extensive experiments across multiple graph datasets to validate the effectiveness of our approach. Our results demonstrate that NvoFDE outperforms traditional constant-order fractional and integer models across a range of tasks, showcasing its superior adaptability and performance.

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NeurIPS Conference 2025 Conference Paper

SALS: Sparse Attention in Latent Space for KV Cache Compression

Junlin Mu
Hantao Huang
Jihang Zhang
Minghui Yu
Tao Wang
Yidong Li

Large Language Models (LLMs) capable of handling extended contexts are in high demand, yet their inference remains challenging due to substantial Key-Value (KV) cache size and high memory bandwidth requirements. Previous research has demonstrated that KV cache exhibits low-rank characteristics within the hidden dimension, suggesting the potential for effective compression. However, due to the widely adopted Rotary Position Embedding (RoPE) mechanism in modern LLMs, naive low‑-rank compression suffers severe accuracy degradation or creates a new speed bottleneck, as the low-rank cache must first be reconstructed in order to apply RoPE. In this paper, we introduce two key insights: first, the application of RoPE to the key vectors increases their variance, which in turn results in a higher rank; second, after the key vectors are transformed into the latent space, they largely maintain their representation across most layers. Based on these insights, we propose the Sparse Attention in Latent Space (SALS) framework. SALS projects the KV cache into a compact latent space via low-rank projection, and performs sparse token selection using RoPE-free query--key interactions in this space. By reconstructing only a small subset of important tokens, it avoids the overhead of full KV cache reconstruction. We comprehensively evaluate SALS on various tasks using two large-scale models: LLaMA2-7b-chat and Mistral-7b, and additionally verify its scalability on the RULER-128k benchmark with LLaMA3. 1-8B-Instruct. Experimental results demonstrate that SALS achieves SOTA performance by maintaining competitive accuracy. Under different settings, SALS achieves 6. 4-fold KV cache compression and 5. 7-fold speed-up in the attention operator compared to FlashAttention2 on the 4K sequence. For the end-to-end throughput performance, we achieves 1. 4-fold and 4. 5-fold improvement compared to GPT-fast on 4k and 32K sequences, respectively. The source code will be publicly available in the future.

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NeurIPS Conference 2024 Conference Paper

DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment

Gongpei Zhao
Tao Wang
Congyan Lang
Yi Jin
Yidong Li
Haibin Ling

Graph neural networks (GNNs) are recognized for their strong performance across various applications, with the backpropagation (BP) algorithm playing a central role in the development of most GNN models. However, despite its effectiveness, BP has limitations that challenge its biological plausibility and affect the efficiency, scalability and parallelism of training neural networks for graph-based tasks. While several non-backpropagation (non-BP) training algorithms, such as the direct feedback alignment (DFA), have been successfully applied to fully-connected and convolutional network components for handling Euclidean data, directly adapting these non-BP frameworks to manage non-Euclidean graph data in GNN models presents significant challenges. These challenges primarily arise from the violation of the independent and identically distributed (i. i. d. ) assumption in graph data and the difficulty in accessing prediction errors for all samples (nodes) within the graph. To overcome these obstacles, in this paper we propose DFA-GNN, a novel forward learning framework tailored for GNNs with a case study of semi-supervised learning. The proposed method breaks the limitations of BP by using a dedicated forward training mechanism. Specifically, DFA-GNN extends the principles of DFA to adapt to graph data and unique architecture of GNNs, which incorporates the information of graph topology into the feedback links to accommodate the non-Euclidean characteristics of graph data. Additionally, for semi-supervised graph learning tasks, we developed a pseudo error generator that spreads residual errors from training data to create a pseudo error for each unlabeled node. These pseudo errors are then utilized to train GNNs using DFA. Extensive experiments on 10 public benchmarks reveal that our learning framework outperforms not only previous non-BP methods but also the standard BP methods, and it exhibits excellent robustness against various types of noise and attacks.

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NeurIPS Conference 2024 Conference Paper

Generated and Pseudo Content guided Prototype Refinement for Few-shot Point Cloud Segmentation

Lili Wei
Congyan Lang
Ziyi Chen
Tao Wang
Yidong Li
Jun Liu

Few-shot 3D point cloud semantic segmentation aims to segment query point clouds with only a few annotated support point clouds. Existing prototype-based methods learn prototypes from the 3D support set to guide the segmentation of query point clouds. However, they encounter the challenge of low prototype quality due to constrained semantic information in the 3D support set and class information bias between support and query sets. To address these issues, in this paper, we propose a novel framework called Generated and Pseudo Content guided Prototype Refinement (GPCPR), which explicitly leverages LLM-generated content and reliable query context to enhance prototype quality. GPCPR achieves prototype refinement through two core components: LLM-driven Generated Content-guided Prototype Refinement (GCPR) and Pseudo Query Context-guided Prototype Refinement (PCPR). Specifically, GCPR integrates diverse and differentiated class descriptions generated by large language models to enrich prototypes with comprehensive semantic knowledge. PCPR further aggregates reliable class-specific pseudo-query context to mitigate class information bias and generate more suitable query-specific prototypes. Furthermore, we introduce a dual-distillation regularization term, enabling knowledge transfer between early-stage entities (prototypes or pseudo predictions) and their deeper counterparts to enhance refinement. Extensive experiments demonstrate the superiority of our method, surpassing the state-of-the-art methods by up to 12. 10% and 13. 75% mIoU on S3DIS and ScanNet, respectively.

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AAAI Conference 2021 Conference Paper

Unsupervised Domain Adaptation for Person Re-identification via Heterogeneous Graph Alignment

Minying Zhang
Kai Liu
Yidong Li
Shihui Guo
Hongtao Duan
Yimin Long
Yi Jin

Unsupervised person re-identification (re-ID) is becoming increasingly popular due to its power in real-world systems such as public security and intelligent transportation systems. However, the person re-ID task is challenged by the problems of data distribution discrepancy across cameras and lack of label information. In this paper, we propose a coarse-tofine heterogeneous graph alignment (HGA) method to find cross-camera person matches by characterizing the unlabeled data as a heterogeneous graph for each camera. In the coarsealignment stage, we assign a projection for each camera and utilize an adversarial learning based method to align coarsegrained node groups from different cameras into a shared space, which consequently alleviates the distribution discrepancy between cameras. In the fine-alignment stage, we exploit potential fine-grained node groups in the shared space and introduce conservative alignment loss functions to constrain the graph aligning process, resulting in reliable pseudo labels as learning guidance. The proposed domain adaptation framework not only improves model generalization on target domain, but also facilitates mining and integrating the potential discriminative information across different cameras. Extensive experiments on benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-arts.

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AAAI Conference 2020 Conference Paper

Domain Adaptive Attention Learning for Unsupervised Person Re-Identification

Yangru Huang
Peixi Peng
Yi Jin
Yidong Li
Junliang Xing

Person re-identiﬁcation (Re-ID) across multiple datasets is a challenging task due to two main reasons: the presence of large cross-dataset distinctions and the absence of annotated target instances. To address these two issues, this paper proposes a domain adaptive attention learning approach to reliably transfer discriminative representation from the labeled source domain to the unlabeled target domain. In this approach, a domain adaptive attention model is learned to separate the feature map into domain-shared part and domainspeciﬁc part. In this manner, the domain-shared part is used to capture transferable cues that can compensate cross-dataset distinctions and give positive contributions to the target task, while the domain-speciﬁc part aims to model the noisy information to avoid the negative transfer caused by domain diversity. A soft label loss is further employed to take full use of unlabeled target data by estimating pseudo labels. Extensive experiments on the Market-1501, DukeMTMC-reID and MSMT17 benchmarks demonstrate the proposed approach outperforms the state-of-the-arts.

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TIST Journal 2020 Journal Article

HERA

Gengyu Lyu
Songhe Feng
Yidong Li
Yi Jin
Guojun Dai
Congyan Lang

Partial label learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with this type of problem by either treating each candidate label equally or identifying the ground-truth label iteratively. In this article, we propose a novel PLL approach named HERA, which simultaneously incorporates the HeterogEneous Loss and the SpaRse and Low-rAnk procedure to estimate the labeling confidence for each instance while training the desired model. Specifically, the heterogeneous loss integrates the strengths of both the pairwise ranking loss and the pointwise reconstruction loss to provide informative label ranking and reconstruction information for label identification, whereas the embedded sparse and low-rank scheme constrains the sparsity of ground-truth label matrix and the low rank of noise label matrix to explore the global label relevance among the whole training data, for improving the learning model. Comprehensive ablation study demonstrates the effectiveness of our employed heterogeneous loss, and extensive experiments on both artificial and real-world datasets demonstrate that our method achieves superior or comparable performance against state-of-the-art methods.

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TIST Journal 2019 Journal Article

Co-saliency Detection with Graph Matching

Zun Li
Congyan Lang
Jiashi Feng
Yidong Li
Tao Wang
Songhe Feng

Recently, co-saliency detection, which aims to automatically discover common and salient objects appeared in several relevant images, has attracted increased interest in the computer vision community. In this article, we present a novel graph-matching based model for co-saliency detection in image pairs. A solution of graph matching is proposed to integrate the visual appearance, saliency coherence, and spatial structural continuity for detecting co-saliency collaboratively. Since the saliency and the visual similarity have been seamlessly integrated, such a joint inference schema is able to produce more accurate and reliable results. More concretely, the proposed model first computes the intra-saliency for each image by aggregating multiple saliency cues. The common and salient regions across multiple images are thus discovered via a graph matching procedure. Then, a graph reconstruction scheme is proposed to refine the intra-saliency iteratively. Compared to existing co-saliency detection methods that only utilize visual appearance cues, our proposed model can effectively exploit both visual appearance and structure information to better guide co-saliency detection. Extensive experiments on several challenging image pair databases demonstrate that our model outperforms state-of-the-art baselines significantly.

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ICRA Conference 2018 Conference Paper

Constrained Confidence Matching for Planar Object Tracking

Tao Wang 0011
Haibin Ling
Congyan Lang
Songhe Feng
Yi Jin 0001
Yidong Li

Tracking planar objects has a wide range of applications in robotics. Conventional template tracking algorithms, however, often fail to observe fast object motion or drift significantly after a period of time, due to drastic object appearance change. To address such challenges, we propose a novel constrained confidence matching algorithm for motion estimation and a robust Kalman filter for template updating. Integrated with an accurate occlusion detector, our approach achieves accurate motion estimation in presence of partial occlusion, by excluding occluded pixels from computation of motion parameters. Furthermore, the proposed Kalman filter employs a novel control-input model to handle the object appearance change, which brings our tracker high robustness against sudden illumination change and heavy motion blur. For evaluation, we compare the proposed tracker with several state-of-the-art planar object trackers on two public benchmark datasets. Experimental results show that our algorithm achieves robust tracking results against various environmental variations, and outperforms baseline algorithms remarkably on both datasets.

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