Fundamentals of Privacy-Preserving Federated Learning

Explore the fundamentals of privacy-preserving federated learning in this 33-minute webinar presented by Ming Ding from Data61, CSIRO for the IEEE Signal Processing Society Information Forensics and Security Technical Committee Webinar Series. Delve into the core concepts and techniques used to protect data privacy while enabling collaborative machine learning across distributed datasets. Gain insights into the challenges and solutions in implementing federated learning systems that maintain confidentiality and security. Discover how this innovative approach allows multiple parties to jointly train models without sharing raw data, addressing critical privacy concerns in various industries and applications. Learn about the latest advancements and potential future directions in this rapidly evolving field of privacy-preserving machine learning.

Fundamentals of Privacy-Preserving Federated Learning