Decentralized Federated Learning over Wireless Channels: A Review

Author:

SATO Koya1

Affiliation:

1. Artificial Intelligent eXploration Research Center, University of Electro-Communications

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference49 articles.

1. 1) B. McMahan, E. Moore, D. Ramage, S. Hampson, and B.A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” Proc. AISTATS, Fort Lauderdale, Florida, USA, April 2017.

2. 2) T. Li, A.K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Process. Mag., vol.37, no.3, pp.50-60, 2020.

3. 3) L.U. Khan, W. Saad, Z. Han, E. Hossain, and C.S. Hong, “Federated learning for internet of things: Recent advances, taxonomy, and open challenges,” IEEE Commun. Surveys Tuts., vol.23, no.3, pp.1759-1799, 2021.

4. 4) C.M. Bishop, Pattern Recognition and Machine Learning, Springer-Verlag, Berlin, Heidelberg, 2006.

5. 5) 斎藤康毅,ゼロから作るDeep Learning—Pythonで学ぶディープラーニングの理論と実装—, O'Reilly Japan, 2016.

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