Fed-RAC: Resource-Aware Clustering for Tackling Heterogeneity of Participants in Federated Learning

Federated Learning is a training framework that enables multiple participants to collaboratively train a shared model while preserving data privacy. The heterogeneity of devices and networking resources of the participants delay the training and aggregation. The paper introduces a novel approach to...

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Veröffentlicht in:	IEEE transactions on parallel and distributed systems 2024-07, Vol.35 (7), p.1207-1220
Hauptverfasser:	Mishra, Rahul, Gupta, Hari Prabhat, Banga, Garvit, Das, Sajal K.
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation models Clustering Clusters Computational modeling Distillation Federated learning Heterogeneity leader-follower technique Mathematical models Optimization Performance evaluation resource aware clustering Servers Training
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