FedGroup: Efficient Federated Learning via Decomposed Similarity-Based Clustering

Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally. Unlike the centralized training setting, the non-IID and imbalanced (statistical heterogeneity) training data of FL is distribute...

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Veröffentlicht in:	2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) S. 228 - 237
Hauptverfasser:	Duan, Moming, Liu, Duo, Ji, Xinyuan, Liu, Renping, Liang, Liang, Chen, Xianzhang, Tan, Yujuan
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.09.2021
Schlagworte:	Collaborative work Distributed Machine Learning Federated Learning Linear approximation Neural networks Performance evaluation Scalability Training Training data
Online-Zugang:	Volltext
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