Communication-Efficient and Privacy-Preserving Verifiable Aggregation for Federated Learning.

Peng, Kaixin; Shen, Xiaoying; Gao, Le; Wang, Baocang; Lu, Yichao

Peng, Kaixin; Shen, Xiaoying; Gao, Le; Wang, Baocang; Lu, Yichao.

Afiliação

Peng K; The Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China.
Shen X; The State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an 710071, China.
Gao L; The Key Laboratory of Cryptography of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, China.
Wang B; The Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China.
Lu Y; The State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an 710071, China.

Entropy (Basel) ; 25(8)2023 Jul 27.

Article em En | MEDLINE | ID: mdl-37628155

Palavras-chave

federated learning; homomorphic hash function; privacy protection; verifiability

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials Idioma: En Revista: Entropy (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China País de publicação: Suíça

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