Federated Learning Based on Dimension Selection - Cheng, Mengyuan (International Journal of Computer Science and Information Technology, 2025)
Federated learning, as a distributed machine learning framework, enables users to collaboratively train models by sharing model parameters without exposing their raw data. However, model parameters may contain privacy-sensitive information, and directly sharing them still poses a risk of user privacy leakage. Local Differential Privacy (LDP) effectively defends against adversaries with arbitrary background knowledge, providing more comprehensive privacy protection. However, the high-dimensional nature of parameters in federated learning presents challenges for the application of LDP. To address this issue, this paper proposes a federated learning algorithm, FDL, that satisfies local differential privacy. The algorithm employs a dimension selection strategy to identify parameter dimensions critical for global aggregation and applies the Laplace mechanism to perturb these dimensions. Compared to traditional methods, the FDL algorithm significantly reduces the number of parameters to be processed and the amount of noise introduced. Theoretical analysis proves that the FDL algorithm satisfies-local differential privacy, and experimental results demonstrate its high usability while ensuring strong privacy protection.