Federated Learning for 6G: Applications, Challenges, and Opportunities

Author:

Yang Zhaohui,Chen Mingzhe,Wong Kai-Kit,Poor H. Vincent,Cui Shuguang

Funder

University College London

National Science Foundation

Princeton University

Engineering and Physical Sciences Research Council

Publisher

Elsevier BV

Subject

General Engineering,Energy Engineering and Power Technology,Materials Science (miscellaneous),General Chemical Engineering,Environmental Engineering,General Computer Science

Reference99 articles.

1. A vision of 6G wireless systems: applications, trends, technologies, and open research problems;Saad;IEEE Netw,2020

2. A joint learning and communications framework for federated learning over wireless networks;Chen;IEEE Trans Wirel Commun,2021

3. Konečný J, McMahan HB, Ramage D, Richtárik P. Federated optimization: distributed machine learning for on-device intelligence. 2016. arXiv:1610.02527.

4. Guest editorial: communication technologies for efficient edge learning;Bennis;IEEE Commun Mag,2020

5. Broadband analog aggregation for low-latency federated edge learning;Zhu;IEEE Trans Wirel Commun,2020

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