Enhancing Byzantine robustness of federated learning via tripartite adaptive authentication

Enhancing Byzantine robustness of federated learning via tripartite adaptive authentication

Abstract Federated learning (FL) is a distributed learning paradigm that enables model training while protecting user privacy. However, frequent communication between the server and clients also provides opportunities for attackers to intercept or tamper with parameters, thereby affecting the global...

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Bibliographic Details
Main Authors: Xiaomeng Li, Yanjun Li, Hui Wan, Cong Wang
Format: Article
Language:English
Published: SpringerOpen 2025-05-01
Series:Journal of Big Data
Subjects:
Federated learning
Adaptive matching
Byzantine robustness
Credibility
Parameter authentication
Reliable communication link
Online Access:https://doi.org/10.1186/s40537-025-01165-y
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https://doi.org/10.1186/s40537-025-01165-y

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