Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryption
This addresses privacy concerns for IoT and mobile services by preventing data leakage in federated learning, though it is incremental as it builds on existing encryption methods.
The paper tackles privacy leakage in federated learning by proposing xMK-CKKS, a multi-key homomorphic encryption protocol that encrypts model updates and requires device collaboration for decryption, preserving model accuracy while reducing computational cost compared to Paillier-based methods and consuming an average of 2.4 Watts.
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.