A SECURE AND ADAPTABLE PRIVACY-PRESERVING FEDERATED LEARNING FRAMEWORK USING MULTI-KEY FULLY HOMOMORPHIC ENCRYPTION
DOI:
https://doi.org/10.48047/p3wypg03Keywords:
privacy preservation; federated learning; multi-key fully homomorphic encryptionAbstract
Federated learning enables collaborative model training without requiring centralized data storage, thereby offering a certain level of privacy protection. However, recent studies indicate that the exchange of model updates, such as gradients or weights, can still lead to unintended leakage of sensitive information. Existing approaches based on single-key homomorphic encryption are insufficient to prevent privacy risks
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