116 citations · 130 across the 4 of their papers we have counts for
5 papers
Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning
Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson
In standard federated learning systems, the parameter server broadcasts the global model to the participating devices in every iteration. Motivated by the temporal correlation betw…
Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach
Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson
Federated learning (FL) has received significant attention in recent years for its advantages in efficient training of machine learning models across distributed clients without di…
Dynamic Scheduling for Federated Edge Learning with Streaming Data
Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson
In this work, we consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy co…
Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks
Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson
Federated Learning (FL) is a collaborative machine learning (ML) framework that combines on-device training and server-based aggregation to train a common ML model among distribute…
Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning
Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson
Federated Learning (FL) is a newly emerged decentralized machine learning (ML) framework that combines on-device local training with server-based model synchronization to train a c…