3 papers
cs.LG2025
Timely Parameter Updating in Over-the-Air Federated Learning
Jiaqi Zhu, Zhongyuan Zhao, Xiao Li +3
Incorporating over-the-air computations (OAC) into the model training process of federated learning (FL) is an effective approach to alleviating the communication bottleneck in FL…
cs.LG2025
Rethinking Federated Learning Over the Air: The Blessing of Scaling Up
Jiaqi Zhu, Bikramjit Das, Yong Xie +2
Federated learning facilitates collaborative model training across multiple clients while preserving data privacy. However, its performance is often constrained by limited communic…
cs.DC2025
Age-Aware Partial Gradient Update Strategy for Federated Learning Over the Air
Ruihao Du, Jiaqi Zhu, Zeshen Li +1
Frequent parameter exchanges between clients and the edge server incur substantial communication overhead, posing a critical bottleneck in federated learning (FL). By exploiting th…