Multiple Parallel Federated Learning via Over-the-Air Computation

This paper investigates multiple parallel federated learning in cellular networks, where a base station schedules several FL tasks in parallel and each task has a group of devices involved. To reduce the communication overhead, over-the-air computation is introduced by utilizing the superposition pr...

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Vydáno v:	IEEE open journal of the Communications Society Ročník 3; s. 1252 - 1264
Hlavní autoři:	Shi, Gaoxin, Guo, Shuaishuai, Ye, Jia, Saeed, Nasir, Dang, Shuping
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Cellular communication Collaborative work Computation Computational modeling Convergence Decoupling Device selection Federated learning Greedy algorithms Impact analysis multiple access channel Optimization over-the-air computation Performance evaluation Radio equipment receiver combiner Receivers Servers Superposition (mathematics) Task analysis Task scheduling
ISSN:	2644-125X, 2644-125X
On-line přístup:	Získat plný text
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