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Keynote Talk: Personalization in Federated Learning: Adaptation and Clustering (Asu Ozdaglar)
Asuman Ozdaglar

Mon Dec 13 01:15 PM -- 02:00 PM (PST) @
in New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership »
Event URL: https://neurips2021workshopfl.github.io/NFFL-2021/schedule.html »

In many machine learning applications, data are collected by a large number of devices, calling for a distributed architecture for learning models. Federated learning (FL) aims to address this challenge by providing a decentralized mechanism for leveraging the individual data and computational power of users. Classical FL relies on a single shared model for users but tends to perform poorly in the presence of data and task heterogeneity across users.

This talk presents various approaches for developing multiple ``personalized” models for heterogeneous users. We first consider a meta-learning approach, where the goal is to generate an initial shared model that users adapt to their tasks using small number of additional local computations. Second, we consider a cluster-based approach which is more appropriate when there is substantial heterogeneity in user data distributions. We propose an algorithm that simultaneously learns cluster identities, while fully operating in a decentralized manner.

Author Information

Asuman Ozdaglar (Massachusetts Institute of Technology)

Asu Ozdaglar received the B.S. degree in electrical engineering from the Middle East Technical University, Ankara, Turkey, in 1996, and the S.M. and the Ph.D. degrees in electrical engineering and computer science from the Massachusetts Institute of Technology, Cambridge, in 1998 and 2003, respectively. She is currently a professor in the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology. She is also the director of the Laboratory for Information and Decision Systems. Her research expertise includes optimization theory, with emphasis on nonlinear programming and convex analysis, game theory, with applications in communication, social, and economic networks, distributed optimization and control, and network analysis with special emphasis on contagious processes, systemic risk and dynamic control. Professor Ozdaglar is the recipient of a Microsoft fellowship, the MIT Graduate Student Council Teaching award, the NSF Career award, the 2008 Donald P. Eckman award of the American Automatic Control Council, the Class of 1943 Career Development Chair, the inaugural Steven and Renee Innovation Fellowship, and the 2014 Spira teaching award. She served on the Board of Governors of the Control System Society in 2010 and was an associate editor for IEEE Transactions on Automatic Control. She is currently the area co-editor for a new area for the journal Operations Research, entitled "Games, Information and Networks. She is the co-author of the book entitled “Convex Analysis and Optimization” (Athena Scientific, 2003).

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