Privacy-Enhanced Zero-Order Federated Learning via xMK-CKKS over Wireless Channels
A multi-key homomorphic encryption scheme for federated learning over wireless channels removes single-key bottlenecks, advancing practical encrypted aggregation for edge scenarios.
Summary written by editorial AI · Source link below
arXiv:2605.30123v3 Announce Type: replace Abstract: Homomorphic encryption (HE) enables privacy-preserving aggregation in federated learning (FL) by allowing the server to operate on encrypted data without decryption. Existing HE-over-the-air (OTA) methods mainly rely on single-key HE schemes and require channel estimation or pre-equalization to compensate for wireless fading. However, single-key HE remains vulnerable to honest-but-curious (HBC) clients holding the shared secret key, while mult
Editorial Analysis
Enterprises pursuing privacy-preserving AI at the edge — especially under GDPR constraints — benefit from multi-key HE removing the single aggregator trust assumption.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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