116 citations · 196 across the 23 of their papers we have counts for
3 papers · 1 filter
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…