Author

Gang Li

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2026

Toward Sustainable Federated Learning: A Learning Incentive Framework Balancing Energy Limits and Fairness

Federated Learning (FL) has gained significant attention for its ability to collaboratively train machine learning models across distributed clients. However, traditional FL approaches often overlook the issues of both participation willingness of clients and their early dropouts due to energy depletions over a long-te...

Gang Li, Si-Wei Xue, Jun Cai et al. · IEEE Transactions on Network... · 0 citations

#reinforcement learning Book Open access Sep 2026

SCPFL: A Safety Reinforcement Learning Framework for Joint Energy-Fairness Optimization in Long-term Federated Learning

Federated Learning (FL) enables heterogeneous clients to collaboratively train a shared model while keeping raw data local. In practical FL systems, client participation is often constrained by limited battery energy, and long-term unselection of clients may lower their willingness to participate, which further reduces...

Si-Wei Xue, Gang Li · Proceedings of the Internati... · 0 citations

2026

Toward Efficient Semi-Asynchronous Federated Learning: A Multi-Factor Grouping and Dual-Level Selection Scheme Under Heterogeneous Environments

Semi-Asynchronous Federated Learning (SAFL) takes advantage of both synchronous and asynchronous FLs to train models. However, existing works in semi-asynchronous FLs fail to fully account for heterogeneities in both data and devices. To address these issues, we first propose a Clustered SAFL (CSAFL) framework and theo...

Gang Li, Yuhang Chen, Jun Cai et al. · IEEE Transactions on Cogniti... · 0 citations