Federated Self-supervised Learning for Heterogeneous Clients. (arXiv:2205.12493v1 [cs.LG])

Federated Learning has become an important learning paradigm due to its
privacy and computational benefits. As the field advances, two key challenges
that still remain to be addressed are: (1) system heterogeneity - variability
in the compute and/or data resources present on each client, and (2) lack of
labeled data in certain federated settings. Several recent developments have
tried to overcome these challenges independently. In this work, we propose a
unified and systematic framework, \emph{Heterogeneous Self-supervised Federated
Learning} (Hetero-SSFL) for …

arxiv learning self-supervised learning supervised learning