Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
Abstract
We study the optimization aspects of personalized Federated Learning (FL). We develop a universal optimization theory applicable to all convex personalized FL models in the literature. In particular, we propose a general personalized objective capable of recovering essentially any existing personalized FL objective as a special case. We design several optimization techniques to minimize the general objective, namely a tailored variant of Local SGD and variants of accelerated coordinate descent/accelerated SVRCD. We demonstrate the practicality and/or optimality of our methods both in terms of communication and local computation. Lastly, we argue about the implications of our general optimization theory when applied to solve specific personalized FL objectives.
Type
Publication
Transactions on Machine Learning Research
Authors
PhD (2020-2025)
Boxin Zhao was a PhD student in Econometrics and Statistics at University of Chicago, Booth School of Business. His research interests include probabilistic graphical models, functional data analysis and distributed learning, with a focus on developing novel methodologies with both practical applications and theoretical guarantees.
Authors
Professor of Data Sciences and Operations
Mladen Kolar is a Professor of Data Sciences and Operations at the University of Southern California Marshall School of Business and a Visiting Professor of Statistics and Data Science at Mohamed bin Zayed University of Artificial Intelligence. Before joining USC, he was on the faculty of the University of Chicago Booth School of Business. His research is focused on high-dimensional statistical methods, graphical models, varying-coefficient models and data mining, driven by the need to uncover interesting and scientifically meaningful structures from observational data. He is a Fellow of the Institute of Mathematical Statistics.