Author

Feilong Lin

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2026

Practical Federated Unlearning: A Target Client-Driven Approach to Model Forgetting

To remove the contribution of specific data from the global model in federated learning, federated unlearning has recently emerged. Existing approaches face challenges such as reliance on full client participation, the need to store historical model updates, and high communication costs. To overcome these limitations,...

Lei Tian, Fei-Long Lin, Zhan Qin et al. · IEEE Transactions on Informa... · 0 citations

2026

Charging Optimization for Mobile Devices With Multi-Agent Reinforcement Learning in Wireless Rechargeable Sensor Networks

Wireless power transfer (WPT) technique is promising for addressing the energy bottleneck of conventional wireless sensor networks (WSNs). Most existing work focuses on optimizing either the deployment of static chargers or the trajectory design of mobile chargers when the device-to-be-charged is static. However, when...

Yihao Shao, Xiuling Zhang, Riheng Jia et al. · IEEE Transactions on Network... · 0 citations

Aug 2026

MoFedAGR: Mitigating client drift with adaptive gradient regularization and global momentum in federated learning.

This work comprehensively considering the effects of client drift during the training process, and quantifying it as the aggregation error, proposes adaptive gradient regularization, which is based on gradient regularization and further and applies different regularization strengths to each parameter based on the magni...

Xiang Wang, Lei Tian, Jiahao Gan et al. · Neural Networks · 0 citations