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A secure aggregation method for federated learning based on homomorphic encryption
- 1 Shenzhen University
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Abstract
The development of cloud computing and big data has promoted the use of cloud servers in machine learning but has also raised concerns about privacy security. To enhance security and efficiency, this paper proposes a multi-key aggregation scheme based on improved Ring Learning With Errors (R-LWE) homomorphic encryption. This method protects the privacy of local model parameters and prevents information leakage through collaborative decryption. Experimental results demonstrate that the proposed scheme can resist collusion attacks, reduce communication overhead, and maintain model accuracy.
Keywords
federated learning, privacy protection, homomorphic encryption
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Cite this article
Ma,X. (2025). A secure aggregation method for federated learning based on homomorphic encryption. Advances in Engineering Innovation,16(2),80-85.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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