no code implementations • 2 May 2023 • 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 constraints.
no code implementations • 14 Dec 2022 • 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 distributed agents.
no code implementations • 23 Jul 2021 • 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 centralized ML model over distributed nodes.