Title: Random Projection Against Gradient Leakage: Privacy-Preserving in Federated Learning
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: federated learning; random projection; privacy preservation

Abstract: Federated learning has emerged as an effective paradigm for collaborative model training under strict data-privacy constraints. However, conventional federated learning remains vulnerable to gradient-based reconstruction attacks, which can expose sensitive information. Existing privacy-preserving methods often incur significant performance degradation. To address this challenge, we propose a algorithm that balances privacy and utility by combining Random Projection Filters (RPFs) with controlled data perturbations. Specifically, during local training, each client first injects optimized noise into the data that minimally affects model performance to enhance privacy. Simultaneously, a subset of convolutional kernels is replaced with random projection filters, which structurally randomize the network and reduce the risk of sensitive data being revealed through gradients. This combination effectively strengthens privacy while inducing only a marginal drop in model accuracy. Extensive experiments demonstrate that our approach substantially improves privacy protection while maintaining high model performance, providing a practical solution for mitigating privacy risks in federated learning.