Quantization-based chained privacy-preserving federated learning

Abstract Federated Learning (FL) is an advanced distributed machine learning framework crucial in protecting data privacy and security. By enabling multiple participants to train models while keeping their data local collaboratively, FL effectively mitigates the risks associated with centralized sto...

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Principais autores:	Ya Liu, Shumin Wu, Yibo Li, Fengyu Zhao, Yanli Ren
Format:	Artigo
Jezik:	Inglês
Izdano:	Nature Portfolio 2025-05-01
Serija:	Scientific Reports
Teme:	Federated learning (FL) Quantization Privacy-preserving Lightweight
Online dostop:	https://doi.org/10.1038/s41598-025-01420-5
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