A privacy-preserving federated learning algorithm for intelligent inspection in pumped storage power station

As a distributed machine learning architecture, Federated Learning (FL) can train a global model by exchanging users' model parameters without their local data. However, with the evolution of eavesdropping techniques, attackers can infer information related to users' local data with the intercepted model parameters, resulting in privacy leakage and hindering the application of FL in smart factories. To meet the privacy protection needs of the intelligent inspection task in pumped storage power stations, in this paper we propose a novel privacy-preserving FL algorithm based on multi-key Fully Homomorphic Encryption (FHE), called MFHE-PPFL. Specifically, to reduce communication costs caused by deploying the FHE algorithm, we propose a self-adaptive threshold-based model parameter compression (SATMPC) method. It can reduce the amount of encrypted data with an adaptive thresholds-enabled user selection mechanism that only enables eligible devices to communicate with the FL server. Moreover, to protect model parameter privacy during transmission, we develop a secret sharing-based multi-key RNS-CKKS (SSMR) method that encrypts the device's uploaded parameter increments and supports decryption in device dropout scenarios. Security analyses and simulation results show that our algorithm can prevent four typical threat models and outperforms the state-of-the-art in communication costs with guaranteed accuracy.