FedGS: A federated group synchronization framework for heterogeneous data

FedGS is a federated group synchronization framework implemented on LEAF-MX. It provides a novel client selection strategy and an innovative group collaborative training protocol based on compound step synchronization to make federated learning excellent on non-independent and identically distribute...

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Bibliographic Details
Published in:Software impacts 2022-05, Vol.12, p.100282, Article 100282
Main Authors: Li, Zonghang, He, Yihong
Format: Article
Language:English
Subjects:
Artificial intelligence
Client selection
Data heterogeneity
Federated learning
Industrial IoT
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Summary:FedGS is a federated group synchronization framework implemented on LEAF-MX. It provides a novel client selection strategy and an innovative group collaborative training protocol based on compound step synchronization to make federated learning excellent on non-independent and identically distributed (non-i.i.d.) data, achieving higher accuracy, lower loss, and faster convergence. FedGS is an open-source, readable, and easy-to-use framework.
ISSN:2665-9638
2665-9638
DOI:10.1016/j.simpa.2022.100282