Privacy‐preserving federated learning based on multi‐key homomorphic encryption

TLDR

Machine learning and IoT raise security and privacy concerns, and while federated learning avoids sending raw data, it still suffers from privacy leakage. This work introduces xMK‑CKKS, an enhanced multi‑key homomorphic encryption protocol, to create a privacy‑preserving federated learning framework. The scheme encrypts model updates with an aggregated public key and requires all devices to collaborate for decryption. The approach blocks privacy leakage, resists collusion among fewer than N−1 devices and the server, and outperforms alternatives in communication and computation while maintaining accuracy.

Abstract

With the advance of machine learning and the Internet of Things (IoT), security and privacy have become critical concerns in mobile services and networks. Transferring data to a central unit violates the privacy of sensitive data. Federated learning mitigates this need to transfer local data by sharing model updates only. However, privacy leakage remains an issue. This paper proposes xMK-CKKS, an improved version of the MK-CKKS multi-key homomorphic encryption protocol, to design a novel privacy-preserving federated learning scheme. In this scheme, model updates are encrypted via an aggregated public key before sharing with a server for aggregation. For decryption, a collaboration among all participating devices is required. Our scheme prevents privacy leakage from publicly shared model updates in federated learning and is resistant to collusion between k < N − 1 participating devices and the server. The evaluation demonstrates that the scheme outperforms other innovations in communication and computational cost while preserving model accuracy.