Representative Learning: From Point Representatives to Distributed AI

Abstract

Distributed machine learning traditionally relies on exchanging gradients, model parameters, or predictions across computing nodes. Such communication can become inefficient and unstable when data are heterogeneous or communication is asynchronous. In this talk, I present Representative Learning (RepL), a framework that replaces model-based communication with the exchange of representative data objects that summarize local statistical information while preserving privacy and interpretability.

I will describe the evolution of RepL from simple point representatives to score-preserving and distribution-valued representations, highlighting recent developments in stability, identifiability, and heterogeneity-aware learning. I will also discuss extensions to collaborative learning, clouded representatives based on lifted empirical risk, and distributed AI systems that communicate information at the data level rather than the model level. The talk concludes with current challenges and future directions toward a unified framework for distributed AI under heterogeneity and communication constraints.

About the speaker

Keren Li is a tenure-track Assistant Professor in the Department of Mathematics at the University of Alabama at Birmingham. He earned his Ph.D. from the University of Illinois Chicago and completed postdoctoral training at Northwestern University. His research centers on distributed machine learning, particularly Representative Learning (RepL), a data-centric framework for decentralized AI that addresses challenges arising from heterogeneity and asynchrony in large-scale distributed environments.