4 papers
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…
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…
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…
Boosting Dynamic TDD in Small Cell Networks by the Multiplicative Weight Update Method
Jiaqi Zhu, Nikolaos Pappas, Howard H. Yang
We leverage the Multiplicative Weight Update (MWU) method to develop a decentralized algorithm that significantly improves the performance of dynamic time division duplexing (D-TDD…