Federated Learning Privacy-preserving Scheme Based on DMK-CKKS
Abstract
This paper proposes a privacy-preserving federated learning (FL) framework by intorducing an enhanced multi-key homomorphic encryption scheme based on MK-CKKS. To address the vulnerabilities of original MK-CKKS against semi-honest participants and server collusion attacks in FL, we redesign the key architecture by introducing an aggregated public key derived from all participants' private keys, and propose the improved Decentralized MK-CKKS(DMK-CKKS) algorithm. This modification eliminates single-point decryption risks while reducing communication rounds through merged aggregation and collaborative decryption phases. The proposed framework ensures computational security under the Ring Learning with Errors (RLWE) assumption and resists collusion attacks involving up to k
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