Min-Max Cost Optimization for Efficient Hierarchical Federated Learning in Wireless Edge Networks

Federated learning is a distributed machine learning technology that can protect users' data privacy, so it has attracted more and more attention in the industry and academia. Nonetheless, most of the existing works focused on the cost optimization of the entire process, while the cost of indiv...

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Vydané v:	IEEE transactions on parallel and distributed systems Ročník 33; číslo 11; s. 2687 - 2700
Hlavní autori:	Feng, Jie, Liu, Lei, Pei, Qingqi, Li, Keqin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.11.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Collaborative work Computational modeling Convergence CPU-cycle frequency Data models Decomposition Federated learning Heuristic methods Iterative algorithms local accuracy Machine learning min-max cost Mixed integer Nonlinear programming Optimization Resource management Servers Smart devices Subcarriers Training wireless edge networks Wireless networks
ISSN:	1045-9219, 1558-2183
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