Privacy Preserving Federated Learning Using CKKS Homomorphic Encryption

With the rapid development of distributed machine learning and Internet of things, tons of distributed data created by devices are used for model training and what comes along is the concern of security and privacy. Traditional method of distributed machine learning asks devices to upload their raw data to a server, which may cause the privacy leakage. Federated learning mitigates this problem by sharing each devices' model parameters only. However, it still has the risk of privacy leakage due to the weak security of model parameters. In this paper, we propose a scheme called privacy enhanced federated averaging (PE-FedAvg) to enhance the security of model parameters. By the way, our scheme achieves the same training effect as Fedavg do at the cost of extra but acceptable time and has better performances on communication and computation cost compared with Paillier based federated averaging. The scheme uses the CKKS homomorphic encryption to encrypt the model parameters, provided by detailed scheme design and security analysis. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted in two real-life datasets, and shows the advantages on aspects of communication and computation. Finally, we discuss the feasibility of deployment on IoT devices.