Safeguarding cross-silo federated learning with local differential privacy

Abstract

Federated Learning (FL) is a new computing paradigm in privacy-preserving Machine Learning (ML), where the ML model is trained in a decentralized manner by the clients, preventing the server from directly accessing privacy-sensitive data from the clients. Unfortunately, recent advances have shown potential risks for user-level privacy breaches under the cross-silo FL framework. In this paper, we propose addressing the issue by using a three-plane framework to secure the cross-silo FL, taking advantage of the Local Differential Privacy (LDP) mechanism. The key insight here is that LDP can provide strong data privacy protection while still retaining user data statistics to preserve its high utility. Experimental results on three real-world datasets demonstrate the effectiveness of our framework.

Keywords

• Federated learning
• Cross-silo
• Local differential privacy
• Perturbation