Web: http://arxiv.org/abs/2209.10537

Sept. 22, 2022, 1:14 a.m. | Junjiao Tian, James Seale Smith, Zsolt Kira

cs.CV updates on arXiv.org arxiv.org

Federated Learning (FL) seeks to distribute model training across local
clients without collecting data in a centralized data-center, hence removing
data-privacy concerns. A major challenge for FL is data heterogeneity (where
each client's data distribution can differ) as it can lead to weight divergence
among local clients and slow global convergence. The current SOTA FL methods
designed for data heterogeneity typically impose regularization to limit the
impact of non-IID data and are stateful algorithms, i.e., they maintain local
statistics over …

arxiv federated learning regularization

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