Efficient and Lightweight Federated Learning Scheme for Privacy Protection and Enhancement

Deep Neural Networks DNNs have been widely used in computer vision, speech recognition, and recommender systems, which require large amounts of user data. However, the collection of data can result in data privacy breaches. Federated learning FL protects data privacy by enabling multiple clients to collaborate in training deep neural network models on private datasets and sharing the training results. However, traditional federated learning solutions are vulnerable to malicious data theft from malicious clients and cloud servers that infer private user data from transmitted intermediate parameters and are unfriendly to resource-constrained clients. In this paper, we propose an effi-cient and privacy-preserving lightweight federated learning PL-FL scheme based on the federated averaging algorithm that combines differential privacy DP and ring-based fully homomorphic encryption FHE . Specifically, we utilize a Gaussian mechanism to perturb the client's local model parameters, on top of which we use ring-based learning of FHE to prevent theft by mali-cious attackers. The formal analysis presented in this paper demonstrates that the proposed scheme can achieve model convergence with reduced communi-cation consumption and time while providing robust privacy protection. Exten-sive experimental results on diverse datasets illustrate that the scheme exhibits competitive model performance and computational efficiency, when compared to the FL baseline. Furthermore, the privacy analysis experiments demonstrate that the approach effectively prevents malicious data theft and recovery, providing strong privacy protection capabilities.