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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Vydané v:	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
Hlavní autori:	Duan, Moming, Liu, Duo, Ji, Xinyuan, Liu, Renping, Liang, Liang, Chen, Xianzhang, Tan, Yujuan
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.09.2021
Predmet:	Collaborative work Distributed Machine Learning Federated Learning Linear approximation Neural networks Performance evaluation Scalability Training Training data
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