Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryption

TL;DR

This paper introduces xMK-CKKS, a multi-key homomorphic encryption protocol for federated learning that enhances privacy, resists collusion, and reduces computational costs, making it suitable for IoT environments.

Contribution

It proposes a novel multi-key homomorphic encryption scheme for privacy-preserving federated learning that improves security and efficiency over existing methods.

Findings

Preserves model accuracy comparable to traditional federated learning.

Reduces computational cost compared to Paillier-based schemes.

Consumes 2.4 Watts of energy, suitable for IoT devices.

Abstract

With the advance of machine learning and the internet of things (IoT), security and privacy have become key concerns in mobile services and networks. Transferring data to a central unit violates privacy as well as protection of sensitive data while increasing bandwidth demands.Federated learning mitigates this need to transfer local data by sharing model updates only. However, data leakage still remains an issue. In this paper, we propose xMK-CKKS, a 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, collaboration between all participating devices is required. This scheme prevents privacy leakage from publicly shared information in federated learning, and is robust to collusion between $k<N-1$ participating devices and the server. Our experimental evaluation demonstrates that the scheme preserves model accuracy against traditional federated learning as well as secure federated learning with homomorphic encryption (MK-CKKS, Paillier) and reduces computational cost compared to Paillier based federated learning. The average energy consumption is 2.4 Watts, so that it is suited to IoT scenarios.