FedMDS: An Efficient Model Discrepancy-Aware Semi-asynchronous Clustered Federated Learning Framework

Federated learning (FL) is an emerging distributed machine learning paradigm that protects privacy and tackles the problem of isolated data islands. At present, there are two main communication strategies of FL: synchronous FL and asynchronous FL. The advantages of synchronous FL are that the model...

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Veröffentlicht in:	IEEE transactions on parallel and distributed systems Jg. 34; H. 3; S. 1 - 14
Hauptverfasser:	Zhang, Yu, Liu, Duo, Duan, Moming, Li, Li, Chen, Xianzhang, Ren, Ao, Tan, Yujuan, Wang, Chengliang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.03.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Algorithms Clients Data models Datasets Distributed Machine Learning Federated learning Machine learning Neural Network Optimization Risk communication Servers Strategy Synchronism Task analysis Time synchronization Training Training data
ISSN:	1045-9219, 1558-2183
Online-Zugang:	Volltext
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