Monday, April 13, 2026 - 4:00pm
Event Calendar Category
LIDS Seminar Series
Speaker Name
Suhas Diggavi
Affiliation
UCLA
Building and Room Number
32-155
"A Statistical Framework and Algorithms for Personalized Federated Learning"
In federated learning, edge nodes collaboratively build learning models from locally generated data. A unique challenge in Federated learning (FL) is heterogeneous data and resources necessitating personalization and limited local data motivating collaboration.
We begin with a statistical framework that unifies several different personalized FL algorithms as well as suggest new algorithms. We demonstrate this framework through personalized learning algorithms, including AdaPeD based on information-geometry regularization, and ADEPT adaptive method that balances local information and collaboration. We examine through this lens, personalized unsupervised learning tasks including diffusion based generative models. We also develop a different methodology for personalized diffusion models called SPIRE, which we show arises from a Gaussian mixture model heterogeneity. This also allows for lightweight adaptation for new users who did not participate in collaboration. We finally present an instantiation of personalized online learning through multi-agent multi-armed bandit problems, where we demonstrate a complete characterization for regret of heterogeneous stochastic linear bandits, demonstrating regimes where collaboration helps.
Parts of this talk are joint work with Kaan Ozkara, Antonious Girgis and Deepesh Data, Ruida Zhou, Bruce Huang.
Suhas Diggavi is currently a Professor of Electrical and Computer Engineering at UCLA. His undergraduate education is from IIT, Delhi and his PhD is from Stanford University. He has worked as a principal member research staff at AT&T Shannon Laboratories and directed the Laboratory for Information and Communication Systems (LICOS) at EPFL. At UCLA, he directs the Information Theory and Systems Laboratory.
His research interests include information theory and its applications to several areas including machine learning, security & privacy, wireless networks, data compression, cyber-physical systems, bio-informatics and neuroscience; more information can be found at http://licos.ee.ucla.edu.
He has received several recognitions for his research from IEEE and ACM, including the 2013 IEEE Information Theory Society & Communications Society Joint Paper Award, the 2021 ACM Conference on Computer and Communications Security (CCS) best paper award, the 2013 ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) best paper award, the 2006 IEEE Donald Fink prize paper award among others. He was selected as a Guggenheim fellow for Natural Sciences in 2021. He also received the 2019 Google Faculty Research Award, 2020 Amazon faculty research award and 2021 Facebook/Meta faculty research award. He served as a IEEE Distinguished Lecturer and also served on board of governors for the IEEE Information theory society (2016-2021). He is a Fellow of the IEEE.
He is the Editor-in-Chief of the IEEE BITS Information Theory Magazine and has been an associate editor for IEEE Transactions on Information Theory, ACM/IEEE Transactions on Networking and other journals and special issues, as well as in the program committees of several IEEE conferences. He has also helped organize IEEE and ACM conferences including serving as the Technical Program Co-Chair for 2012 IEEE Information Theory Workshop (ITW), the Technical Program Co-Chair for the 2015 IEEE International Symposium on Information Theory (ISIT) and General co-chair for ACM Mobihoc 2018. He has 8 issued patents.