FL-EASGD: Federated Learning Privacy Security Method Based on Homomorphic Encryption

Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext data. However, there is still a potential risk of privacy leakage, for example, attackers can obtain the original data through model inference attacks. Therefore, safeguarding the privacy of model parameters becomes crucial. One proposed solution involves incorporating homomorphic encryption algorithms into the federated learning process. However, the existing federated learning privacy protection scheme based on homomorphic encryption will greatly reduce the efficiency and robustness when there are performance differences between parties or abnormal nodes. To solve the above problems, this paper proposes a privacy protection scheme named Federated Learning-Elastic Averaging Stochastic Gradient Descent (FL-EASGD) based on a fully homomorphic encryption algorithm. First, this paper introduces the homomorphic encryption algorithm into the FL-EASGD scheme to prevent model plaintext leakage and realize privacy security in the process of model aggregation. Second, this paper designs a robust model aggregation algorithm by adding time variables and constraint coefficients, which ensures the accuracy of model prediction while solving performance differences such as computation speed and node anomalies such as downtime of each participant. In addition, the scheme in this paper preserves the independent exploration of the local model by the nodes of each party, making the model more applicable to the local data distribution. Finally, experimental analysis shows that when there are abnormalities in the participants, the efficiency and accuracy of the whole protocol are not significantly affected.