9.11 - 3:00pm Western European Time (UTC+1) (09:00am US Eastern Coast and 10:00pm Chinese Time)
Ming Ding, Principal Research Scientist at Data61, CSIRO, in Sydney, NSW, Australia.

Fundamentals of Privacy-Preserving Federated Learning
Abstract: 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.