[Colloquium] Tomorrow: Bo Li (UIUC) – Trustworthy Federated Learning: Robustness, Fairness, Privacy, and Their Interconnections

[Rob Mitchum]

*Computer Science/Data Science Institute Distinguished Speaker Series
<https://datascience.uchicago.edu/news/autumn-2022-distinguished-speaker-series/>*

*Bo Li*
*Assistant Professor, Department of Computer Science*
*University of Illinois, Urbana-Champaign*

*Friday, October 7th *
*12:00pm - 1:30pm (12:00 lunch, 12:30 talk)*
*In Person: John Crerar Library 390*
*Zoom: *
https://uchicagogroup.zoom.us/j/92530763883?pwd=b3ZmcTA4RlEzTFdsTVd4QWE5bHVJQT09
*Meeting ID*: 925 3076 3883
*Passcode*: 138025

*Trustworthy Federated Learning: Robustness, Fairness, Privacy, and Their
Interconnections
<https://datascience.uchicago.edu/events/bo-li-uiuc-trustworthy-federated-learning-robustness-fairness-privacy-and-their-interconnections/>*

*Abstract: *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.

*Bio: *Dr. Bo Li <https://cs.illinois.edu/about/people/faculty/lbo> 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.


-- 
*Rob Mitchum*

*Associate Director of Communications for Data Science and Computing*
*Department of Computer Science*
*Data Science Institute*
*University of Chicago*
*rmitchum at uchicago.edu <rmitchum at ci.uchicago.edu>*
*773-484-9890*
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