Part of the DSI Autumn 2022 Distinguished Speaker Series.
Advances in machine learning have led to rapid and widespread deployment of ML algorithms for safety-critical applications, such as autonomous driving and medical diagnostics. Current machine learning systems, however, assume that training and test data follow the same, or similar, distributions, and do not consider active adversaries manipulating either distribution. Recent work has demonstrated that motivated adversaries can circumvent ML detection models at test time through evasion attacks, or inject well-crafted malicious instances into training data to induce errors during inference through poisoning attacks, especially in the distributed setting. In this talk, I will describe my recent research about security, privacy, and fairness problems in federated learning, with a focus on certifiably robust federated learning against training-time attacks, fairness, and the interconnection between robustness and privacy in federated learning. I will also discuss other defense principles towards developing practical trustworthy federated learning systems with guarantees.
Join us for lunch at 12; Dr. Li’s talk will begin at 12:30. This talk will also be broadcast via Zoom. Please register to receive viewing information.
Dr. Bo Li is an assistant professor in the Department of Computer Science at the University of Illinois at Urbana–Champaign. She is the recipient of the IJCAI Computers and Thought Award, Alfred P. Sloan Research Fellowship, NSF CAREER Award, MIT Technology Review TR-35 Award, Dean’s Award for Excellence in Research, C.W. Gear Outstanding Junior Faculty Award, Intel Rising Star award, Symantec Research Labs Fellowship, Rising Star Award, Research Awards from Tech companies such as Amazon, Facebook, Intel, and IBM, and best paper awards at several top machine learning and security conferences. Her research focuses on both theoretical and practical aspects of trustworthy machine learning, security, machine learning, privacy, and game theory. She has designed several scalable frameworks for trustworthy machine learning and privacy-preserving data publishing systems. Her work has been featured by major publications and media outlets such as Nature, Wired, Fortune, and New York Times.