From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection

arXiv:2404.00371v1 Announce Type: new
Abstract: Federated learning (FL) is an appealing paradigm for learning a global model among distributed clients while preserving data privacy. Driven by the demand for high-quality user experiences, evaluating the well-trained global model after the FL process is crucial. In this paper, we propose a closed-loop model analytics framework that allows for effective evaluation of the trained global model using clients' local data. To address the challenges posed by system and data heterogeneities in the FL …

abstract analytics arxiv client cs.lg data data privacy demand distributed eess.sp federated learning global improving paper paradigm privacy process quality type