Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

There is an increasing interest in a fast-growing machine learning technique called Federated Learning (FL), in which the model training is distributed over mobile user equipment (UEs), exploiting UEs' local computation and training data. Despite its advantages such as preserving data privacy,...

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Vydané v:	IEEE/ACM transactions on networking Ročník 29; číslo 1; s. 398 - 409
Hlavní autori:	Dinh, Canh T., Tran, Nguyen H., Nguyen, Minh N. H., Hong, Choong Seon, Bao, Wei, Zomaya, Albert Y., Gramoli, Vincent
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.02.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Computational modeling Convergence Data models Distributed machine learning Energy consumption Federated learning Heterogeneity Machine learning Optimization optimization decomposition Power consumption Resource allocation Resource management Tradeoffs Training Wireless communication Wireless networks
ISSN:	1063-6692, 1558-2566
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