FedPD: A Federated Learning Framework With Adaptivity to Non-IID Data

Federated Learning (FL) is popular for communication-efficient learning from distributed data. To utilize data at different clients without moving them to the cloud, algorithms such as the Federated Averaging (FedAvg) have adopted a computation then aggregation model, in which multiple local updates...

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Vydané v:	IEEE transactions on signal processing Ročník 69; s. 6055 - 6070
Hlavní autori:	Zhang, Xinwei, Hong, Mingyi, Dhople, Sairaj, Yin, Wotao, Liu, Yang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Agglomeration Algorithms Communication Complexity theory Computational modeling convergence analysis data heterogeneity Data models Design optimization Distributed algorithms Distributed databases Federated learning Heterogeneity machine learning algorithms Servers Signal processing algorithms
ISSN:	1053-587X, 1941-0476
On-line prístup:	Získať plný text
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