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Asynchronous Online Federated Learning With Reduced Communication Requirements

  • Published:2023-12-01 Issue:23 Volume:10 Page:20761-20775
  • ISSN:2327-4662
  • Container-title:IEEE Internet of Things Journal
  • language:
  • Short-container-title:IEEE Internet Things J.

Author:

Gauthier Francois1ORCID,Gogineni Vinay Chakravarthi1ORCID,Werner Stefan1ORCID,Huang Yih-Fang2ORCID,Kuh Anthony3ORCID

Affiliation:

1. Department of Electronic Systems, Norwegian University of Science and Technology, Trondheim, Norway

2. Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN, USA

3. Department of Electrical Engineering, University of Hawaii at Manoa, Honolulu, HI, USA

Funder

Norges Forskningsråd

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Reference51 articles.

1. On the convergence of FedAvg on non-iid data;li;arXiv 1907 02189,2019

2. FedADC: Accelerated Federated Learning with Drift Control

3. COFEL: Communication-Efficient and Optimized Federated Learning with Local Differential Privacy

4. Federated learning: Strategies for improving communication efficiency;kone?n?;arXiv 1610 05492,2016

5. Federated Learning with Non-IID Data in Wireless Networks

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1. Client Selection in Hierarchical Federated Learning;IEEE Internet of Things Journal;2024-09-01
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