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Rethinking Factor Sharing in Federated LoRA: A Rank-Aware Adaptive Approach
Xinyi Xu*, Bingnan Xiao*, Shuang Qin, Gang Feng, and Tony Q. S. Quek. (*Equal contribution)
arXiv:2608.09742, 2026.
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Explainable AI for Next-Generation Wireless Physical Layer: Basics, State-of-the-Art, and Open Challenges
Bingnan Xiao, Shuyan Hu, Xiaojing Chen, Zhiyuan Zhai, Bingcong Li, Wei Ni, Xin Wang, and Ekram Hossain.
Submitted to IEEE Communications Surveys & Tutorials (SCI Q1 Top); arXiv:2606.24424, 2026.
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Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems
Bingnan Xiao, Chenhao Yang, Wei Ni, Xin Wang, and Tony Q. S. Quek.
Submitted to IEEE Transactions on Mobile Computing (CCF-A); arXiv:2606.24416, 2026.
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FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learning
Bingnan Xiao, Yuan Gao, Bingcong Li, Wei Ni, Xin Wang, and Tony Q. S. Quek.
arXiv:2605.09144, 2026.
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Divergence-Based Adaptive Aggregation for Byzantine Robust Federated Learning
Bingnan Xiao, Feng Zhu, Jingjing Zhang, Wei Ni, and Xin Wang.
IEEE Transactions on Information Forensics and Security, 2026. (CCF-A)
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FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks
Bingnan Xiao, Jingjing Zhang, Wei Ni, and Xin Wang.
IEEE Transactions on Wireless Communications, 2025. (SCI Q1 Top)
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Over-the-Air Federated Learning: Status Quo, Open Challenges, and Future Directions
Bingnan Xiao, Xichen Yu, Wei Ni, Xin Wang, and H. Vincent Poor.
Fundamental Research, 2024.
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Optimal Adaptive Power Control for Over-The-Air Federated Edge Learning Under Fading Channels
Xichen Yu, Bingnan Xiao, Wei Ni, and Xin Wang.
IEEE Transactions on Communications, 2023.