Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning

arXiv:2402.12537v1 Announce Type: new
Abstract: Statistical heterogeneity of clients' local data is an important characteristic in federated learning, motivating personalized algorithms tailored to the local data statistics. Though there has been a plethora of algorithms proposed for personalized supervised learning, discovering the structure of local data through personalized unsupervised learning is less explored. We initiate a systematic study of such personalized unsupervised learning by developing algorithms based on optimization criteria inspired by a hierarchical Bayesian statistical framework. We develop adaptive …

abstract algorithms arxiv bayes cs.lg data federated learning hierarchical personalized statistical statistics supervised learning through type unsupervised unsupervised learning