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publications
Multi-level Federated Graph Learning and Self-attention Based Personalized Wi-Fi Indoor Fingerprint Localization
Published in IEEE Communications Letters, 2022
This work studies personalized Wi-Fi indoor fingerprint localization with federated graph learning.
Recommended citation: Zheshun Wu, Xiaoping Wu, and Yunliang Long. (2022). Multi-level Federated Graph Learning and Self-attention Based Personalized Wi-Fi Indoor Fingerprint Localization. IEEE Communications Letters.
Prediction Based Semi-supervised Online Personalized Federated Learning for Indoor Localization
Published in IEEE Sensors Journal, 2022
This work studies semi-supervised online personalized federated learning for indoor localization.
Recommended citation: Zheshun Wu, Xiaoping Wu, and Yunliang Long. (2022). Prediction Based Semi-supervised Online Personalized Federated Learning for Indoor Localization. IEEE Sensors Journal.
Joint Scheduling and Robust Aggregation for Federated Localization over Unreliable Wireless D2D Networks
Published in IEEE Transactions on Network and Service Management, 2023
This work studies joint scheduling and robust aggregation for federated localization.
Recommended citation: Zheshun Wu, Xiaoping Wu, and Yunliang Long. (2023). Joint Scheduling and Robust Aggregation for Federated Localization over Unreliable Wireless D2D Networks. IEEE Transactions on Network and Service Management.
Information-Theoretic Generalization Analysis for Topology-aware Heterogeneous Federated Edge Learning over Noisy Channels
Published in IEEE Signal Processing Letters, 2024
This work provides a generalization analysis for topology-aware heterogeneous federated edge learning.
Recommended citation: Zheshun Wu, Zenglin Xu, Hongfang Yu, and Jie Liu. (2024). Information-Theoretic Generalization Analysis for Topology-aware Heterogeneous Federated Edge Learning over Noisy Channels. IEEE Signal Processing Letters.
Topology Learning for Heterogeneous Decentralized Federated Learning Over Unreliable D2D Networks
Published in IEEE Transactions on Vehicular Technology, 2024
This work studies topology learning for decentralized federated learning over unreliable D2D networks.
Recommended citation: Zheshun Wu, Zenglin Xu, Dun Zeng, Junfan Li, and Jie Liu. (2024). Topology Learning for Heterogeneous Decentralized Federated Learning Over Unreliable D2D Networks. IEEE Transactions on Vehicular Technology.
Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients
Published in IEEE Transactions on Neural Networks and Learning Systems, 2024
This work investigates federated generalization for non-participating clients.
Recommended citation: Zheshun Wu, Zenglin Xu, Dun Zeng, Qifan Wang, and Jie Liu. (2024). Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients. IEEE Transactions on Neural Networks and Learning Systems.
On the Necessity of Collaboration for Online Model Selection with Decentralized Data
Published in Advances in Neural Information Processing Systems (NeurIPS 2024), 2024
This work studies the necessity of collaboration for online model selection with decentralized data.
Recommended citation: Junfan Li, Zheshun Wu, Zenglin Xu, and Irwin King. (2024). On the Necessity of Collaboration for Online Model Selection with Decentralized Data. In NeurIPS.
Online Optimization for Learning to Communicate over Time-correlated Channels
Published in IEEE Transactions on Cognitive Communications and Networking, 2025
This work studies online optimization for communication over time-correlated channels.
Recommended citation: Zheshun Wu, Junfan Li, Zenglin Xu, Sumei Sun, and Jie Liu. (2025). Online Optimization for Learning to Communicate over Time-correlated Channels. IEEE Transactions on Cognitive Communications and Networking.
Bandit Learning in Matching Markets Robust to Adversarial Corruptions
Published in International Conference on Learning Representations (ICLR 2026), 2026
This work studies robust bandit learning in two-sided matching markets under adversarial corruptions.
Recommended citation: Zheshun Wu, Jinhang Zuo, Zenglin Xu, and Fang Kong. (2026). Bandit Learning in Matching Markets Robust to Adversarial Corruptions. In ICLR.
Enhancing Progressive Ensemble Learning via Normalized Extra-Gradient Initialization
Published in Neural Networks, 2026
This work develops Normalized Extra-Gradient Initialization to accelerate progressive training of sparse MoE models.
Recommended citation: Zheshun Wu, Yu Pan, Dun Zeng, Zenglin Xu, Qifan Wang, and Jie Liu. (2026). Enhancing Progressive Ensemble Learning via Normalized Extra-Gradient Initialization. Neural Networks.
Federated Combinatorial Causal Bandits with Heterogeneous Causal Influences
Published in Conference on Uncertainty in Artificial Intelligence (UAI 2026), 2026
This work studies federated combinatorial causal bandits under heterogeneous causal influences.
Recommended citation: Zheshun Wu, Wei Chen, Zenglin Xu, and Fang Kong. (2026). Federated Combinatorial Causal Bandits with Heterogeneous Causal Influences. In UAI.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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