Adaptive Federated Learning on Non-IID Data With Resource Constraint

Federated learning (FL) has been widely recognized as a promising approach by enabling individual end-devices to cooperatively train a global model without exposing their own data. One of the key challenges in FL is the non-independent and identically distributed (Non-IID) data across the clients, w...

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Veröffentlicht in:	IEEE transactions on computers Jg. 71; H. 7; S. 1655 - 1667
Hauptverfasser:	Zhang, Jie, Guo, Song, Qu, Zhihao, Zeng, Deze, Zhan, Yufeng, Liu, Qifeng, Akerkar, Rajendra
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.07.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Agglomeration batch size adaption Clients Computational modeling Data models Data privacy Deep learning deep reinforcement learning Distributed databases Federated learning Model accuracy non-IID data Servers Training
ISSN:	0018-9340, 1557-9956
Online-Zugang:	Volltext
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