A Closer Look at Personalization in Federated Image Classification. (arXiv:2204.11841v1 [cs.LG])

Federated Learning (FL) is developed to learn a single global model across
the decentralized data, while is susceptible when realizing client-specific
personalization in the presence of statistical heterogeneity. However, studies
focus on learning a robust global model or personalized classifiers, which
yield divergence due to inconsistent objectives. This paper shows that it is
possible to achieve flexible personalization after the convergence of the
global model by introducing representation learning. In this paper, we first
analyze and determine that non-IID data …

arxiv classification image personalization