Author name cluster

Weidong Bao

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5 papers

1 author row

AAAI Conference 2026 Conference Paper

A Causal Target for Learning to Defer Under Hidden Confounding

Yanmin Li
Lihua Liu
Xin Wang
Zhilong Mao
Jibing Wu
Weidong Bao

Learning decision policies from confounded observational data is a challenging task in causal inference, as unobserved confounders can lead to biased or suboptimal actions when relying solely on machine learning models. A synergistic approach is learning to defer, which decides when to act itself and when to defer to a human expert with access to unobserved information. However, constructing the learning target, which defines the probability of choosing each action or deferral, remains a core challenge. To address this, we propose causal-target-based learning to defer (CTLD) framework, where the causal target is constructed from sharp bounds on potential outcomes. Specifically, the degree of overlap between these bounds determines the probability of deferral, while their relative positions and widths define the probabilities over actions. CTLD aligns model predictions with this causal target to make probabilistic decisions over actions and deferral. We present comprehensive theoretical guarantees for the learned policy and demonstrate the effectiveness of CTLD on synthetic and semi-synthetic datasets.

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AAMAS Conference 2025 Conference Paper

FedHPD: Heterogeneous Federated Reinforcement Learning via Policy Distillation

Wenzheng Jiang
Ji Wang
Xiongtao Zhang
Weidong Bao
Cheston Tan
Flint Xiaofeng Fan

This paper focuses on Federated Reinforcement Learning (FedRL) in black-box settings with heterogeneous agents. Existing studies mostly assume agent homogeneity and knowability of internal details. To tackle these issues, we propose Federated Heterogeneous Policy Distillation (FedHPD). FedHPD uses action probability distributions as a medium for knowledge sharing among heterogeneous agents. Extensive experiments show that FedHPD achieves significant improvements across various benchmark tasks.

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NeurIPS Conference 2025 Conference Paper

Gains: Fine-grained Federated Domain Adaptation in Open Set

Zhengyi Zhong
Wenzheng Jiang
Weidong Bao
Ji Wang
Qi Wang
Guanbo Wang
Yongheng Deng
Ju Ren

Conventional federated learning (FL) assumes a closed world with a fixed total number of clients. In contrast, new clients continuously join the FL process in real-world scenarios, introducing new knowledge. This raises two critical demands: detecting new knowledge, i. e. , knowledge discovery, and integrating it into the global model, i. e. , knowledge adaptation. Existing research focuses on coarse-grained knowledge discovery, and often sacrifices source domain performance and adaptation efficiency. To this end, we propose a fine-grained federated domain adaptation approach in open set (Gains). Gains splits the model into an encoder and a classifier, empirically revealing features extracted by the encoder are sensitive to domain shifts while classifier parameters are sensitive to class increments. Based on this, we develop fine-grained knowledge discovery and contribution-driven aggregation techniques to identify and incorporate new knowledge. Additionally, an anti-forgetting mechanism is designed to preserve source domain performance, ensuring balanced adaptation. Experimental results on multi-domain datasets across three typical data-shift scenarios demonstrate that Gains significantly outperforms other baselines in performance for both source-domain and target-domain clients. Code is available at: https: //github. com/Zhong-Zhengyi/Gains.

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TIST Journal 2022 Journal Article

FLEE: A Hierarchical Federated Learning Framework for Distributed Deep Neural Network over Cloud, Edge, and End Device

Zhengyi Zhong
Weidong Bao
Ji Wang
Xiaomin Zhu
Xiongtao Zhang

With the development of smart devices, the computing capabilities of portable end devices such as mobile phones have been greatly enhanced. Meanwhile, traditional cloud computing faces great challenges caused by privacy-leakage and time-delay problems, there is a trend to push models down to edges and end devices. However, due to the limitation of computing resource, it is difficult for end devices to complete complex computing tasks alone. Therefore, this article divides the model into two parts and deploys them on multiple end devices and edges, respectively. Meanwhile, an early exit is set to reduce computing resource overhead, forming a hierarchical distributed architecture. In order to enable the distributed model to continuously evolve by using new data generated by end devices, we comprehensively consider various data distributions on end devices and edges, proposing a hierarchical federated learning framework FLEE, which can realize dynamical updates of models without redeploying them. Through image and sentence classification experiments, we verify that it can improve model performances under all kinds of data distributions, and prove that compared with other frameworks, the models trained by FLEE consume less global computing resource in the inference stage.

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AAAI Conference 2019 Conference Paper

Private Model Compression via Knowledge Distillation

Ji Wang
Weidong Bao
Lichao Sun
Xiaomin Zhu
Bokai Cao
Philip S. Yu

The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far surpassing mobile devices’ capacity. What is worse, app service providers need to collect and utilize a large volume of users’ data, which contain sensitive information, to build the sophisticated DNN models. Directly deploying these models on public mobile devices presents prohibitive privacy risk. To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA. Following the knowledge distillation paradigm, we jointly use hint learning, distillation learning, and self learning to train a compact and fast neural network. The knowledge distilled from the cumbersome model is adaptively bounded and carefully perturbed to enforce differential privacy. We further propose an elegant query sample selection method to reduce the number of queries and control the privacy loss. A series of empirical evaluations as well as the implementation on an Android mobile device show that RONA can not only compress cumbersome models efficiently but also provide a strong privacy guarantee. For example, on SVHN, when a meaningful (9. 83, 10−6 )-differential privacy is guaranteed, the compact model trained by RONA can obtain 20× compression ratio and 19× speed-up with merely 0. 97% accuracy loss.

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