Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates

Federated Learning (FL) is an emerging decentralized learning framework through which multiple clients can collaboratively train a learning model. However, a major obstacle that impedes the wide deployment of FL lies in massive communication traffic. To train high dimensional machine learning models...

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Vydané v:	IEEE journal on selected areas in communications Ročník 39; číslo 8; s. 2572 - 2589
Hlavní autori:	Cui, Laizhong, Su, Xiaoxin, Zhou, Yipeng, Pan, Yi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation models Algorithms Centroids Clients Clustering Communication Communications traffic Compressing Compression algorithms Computational modeling Convergence convergence rate Data models Federated learning Machine learning Model accuracy model update compression Traffic models Training Transmission Uplink
ISSN:	0733-8716, 1558-0008
On-line prístup:	Získať plný text
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