Personalized Federated Diffusion models and online learning with connections to privacy

Statistical heterogeneity of data in FL has motivated the design of personalized learning, where individual (personalized) models are trained, through collaboration. We build on a statistical framework to propose adaptive methods called ADEPT, which balance 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, supporting privacy through data minimization directly. We finally  focus on online learning, where we first present privacy for multi-arm bandit problems. Then we 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. 

Parts of this work are joint with Kaan Ozkara, Ruida Zhou, Bruce Huang and Antonious Girgis.