Salman Avestimehr: "Secure, Scalable, and Efficient Federated Learning"

FedML: A Secure, Scalable, and Efficient Edge-Cloud Platform for Federated Learning

Abstract: Federated learning (FL) has emerged as a promising approach to to enable decentralized machine learning directly at the edge, in order to enhance users' privacy, comply with regulations, and reduce development costs. In this talk, I will provide an overview of FL and highlight several key research directions in this area. In particular, I discuss four important research directions: (1) privacy and security guarantees of FL; (2) FL over resource-constrained edge nodes; (3) label scarcity and self-supervised FL; and (4) scalable system design for FL. In the second part of the talk, I will provide an overview of FedML (https://fedml.ai), which is a machine learning platform that enables zero-code, lightweight, cross-platform, and provably secure federated learning and analytics. In particular, I highlight four key components of FedML platform: (1) an open-source community of more than 1k users; (2) a lightweight and cross-platform Edge AI SDK for deployment over GPUs, smartphones, and IoTs; (3) a user-friendly MLOps platform to simplify collaboration and real-world deployment; and (4) its diverse applications ecosystem (computer vision, natural language processing, data mining, and time-series forecasting).

Bio: Salman Avestimehr (https://www.avestimehr.com) is the CEO and co-founder of FedML. He is also a Dean's Professor and the inaugural director of the USC-Amazon Center on Trustworthy AI at the ECE and CS Department of University of Southern California. His research interests include decentralized and federated machine learning, information theory, security, and privacy. Dr. Avestimehr has received many awards for his research, including the Presidential PECASE award from the White House (President Obama), the James L. Massey Research & Teaching Award from IEEE Information Theory Society, an Information Theory Society and Communication Society Joint Paper Award, , and several Best Paper Awards at Conferences. He has been an Amazon Scholar in Alexa-AI, and is a fellow of the IEEE.