A Security-Enhanced Federated Learning Scheme Based on Homomorphic Encryption and Secret Sharing

Abstract

Although federated learning is gaining prevalence in smart sensor networks, substantial risks to data privacy and security persist. An improper application of federated learning techniques can lead to critical privacy breaches. Practical and effective privacy-enhanced federated learning (PEPFL) is a widely used federated learning framework characterized by low communication overhead and efficient encryption and decryption processes. Initially, our analysis scrutinized security vulnerabilities within the PEPFL framework and identified an effective attack strategy. This strategy enables the server to derive private keys from content uploaded by participants, achieving a 100% success rate in extracting participants’ private information. Moreover, when the number of participants does not exceed 300, the attack time does not surpass 3.72 s. Secondly, this paper proposes a federated learning model that integrates homomorphic encryption and secret sharing. By using secret sharing among participants instead of secure multi-party computation, the amount of effective information available to servers is reduced, thereby effectively preventing servers from inferring participants’ private gradients. Finally, the scheme was validated through experiments, and it was found to significantly reduce the inherent collusion risks unique to the federated learning scenario. Moreover, even if some participants are unavailable, the reconstructable nature of secret sharing ensures that the decryption process can continue uninterrupted, allowing the remaining users to proceed with further training. Importantly, our proposed scheme exerts a negligible impact on the accuracy of model training.