DP-FedCMRS: Privacy-Preserving Federated Learning Algorithm to Solve Heterogeneous Data

In federated learning, non-independently and non-identically distributed heterogeneous data on the clients can limit both the convergence speed and model utility of federated learning, and gradients can be used to infer original data, posing a threat to user privacy. To address these issues, this pa...

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Detalhes bibliográficos

Principais autores:	Yang Zhang, Shigong Long, Guangyuan Liu, Junming Zhang
Formato:	Artigo
Idioma:	Inglês
Publicado em:	IEEE 2025-01-01
coleção:	IEEE Access
Assuntos:	Federated learning differential privacy non-IID data clustering
Acesso em linha:	https://ieeexplore.ieee.org/document/10910083/
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