Plenary Talk at Sydney Privacy Workshop

Ming Ding has been invited to give a Plenary Talk at the Sydney Privacy Workshop

“Fundamentals of Privacy-Preserving Federated Learning”

Federated learning (FL) is gaining popularity as a decentralized machine learning method. It safeguards client data from direct exposure to external threats. However, attackers can still steal information from shared FL models. To address this, we’ve created a privacy-preserving FL framework using differential privacy (DP). Additionally, we establish a convergence upper-bound for the proposed DP-FL framework, revealing the existence of an optimal number of communication rounds for best convergence with privacy protection.