Federated Learning with Flexible Control
Author:
Affiliation:
1. IBM T. J. Watson Research Center,Yorktown Heights,NY,USA
2. Army Research Laboratory,Adelphi,MD,USA
3. University of Utah,Department of ECE,Salt Lake City,UT,USA
Funder
Arm
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10228851/10228852/10229070.pdf?arnumber=10229070
Reference50 articles.
1. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
2. Optimal Rate Adaption in Federated Learning with Compressed Communications
3. Optimizing Federated Learning on Non-IID Data with Reinforcement Learning
4. CMFL: Mitigating Communication Overhead for Federated Learning
5. GRACE: A Compressed Communication Framework for Distributed Machine Learning
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