Abstract: Federated learning is often touted as a training paradigm that preserves user privacy. In this talk, I’ll discuss ways that federated protocols leak user information, and ways that malicious actors can exploit federated protocols to scrape information from users. If time permits, I’ll also discuss how recent advances in data poisoning can manipulate datasets to preserve privacy by preventing data from being used for model training.

Bio: Tom Goldstein is the Perotto Associate Professor of Computer Science at the University of Maryland. His research lies at the intersection of machine learning and optimization, and targets applications in computer vision and signal processing. Before joining the faculty at Maryland, Tom completed his PhD in Mathematics at UCLA, and was a research scientist at Rice University and Stanford University. Professor Goldstein has been the recipient of several awards, including SIAM’s DiPrima Prize, a DARPA Young Faculty Award, a JP Morgan Faculty award, and a Sloan Fellowship.