Federated Learning with Lossy Distributed Source Coding: Analysis and Optimization

Recently, federated learning (FL), which replaces data sharing with model sharing, has emerged as an efficient and privacy-friendly machine learning (ML) paradigm. One of the main challenges in FL is the huge communication cost for model aggregation. Many compression/quantization schemes have been p...

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Vydané v:	IEEE transactions on communications Ročník 71; číslo 8; s. 1
Hlavní autori:	Yang, Huiyuan, Ding, Tian, Yuan, Xiaojun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Analytical models Berger-Tung coding Communication Computational modeling Convergence Costs Distortion distributed source coding Federated learning Machine learning majorization-minimization model aggregation Optimization Questions Rate-distortion rate-distortion theory Uplink
ISSN:	0090-6778, 1558-0857
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