Security and Robustness of Collaborative Learning Systems

Thu 23Feb2023

Anwar Hithnawi, ETH Zürich

From 11:00 until 12.30

At CAB H 52 (Seminar) + CNB/F/110 (Lunch) , ETH Zurich

CAB H 52 (Seminar) + CNB/F/110 (Lunch), ETH Zurich


In recent years, secure collaborative machine learning paradigms have emerged as a viable option for sensitive applications. By eliminating the need to centralize data, these paradigms protect data sovereignty and reduce risks associated with large-scale data collection. However, they also expose the learning process to active attackers, amplifying robustness issues. In this talk, I'll discuss the security and robustness challenges of secure collaborative learning systems, present our efforts to mitigate some of these issues, and highlight why a definitive solution to robustness in these systems is challenging.

Join us in CAB H 52 (Seminar) + CNB/F/110 (Lunch).

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