This paper demonstrates that applying adaptive latent-space constraints in personalized FL algorithms improves performance across a number of challenging benchmark tasks, especially those with significant feature heterogeneity

Local SGD converges faster under low second-order heterogeneity, and we prove it with tight bounds and supporting experiments.

NormFit is a novel fine-tuning framework designed specifically for few-shot federated learning scenarios characterized by heterogeneous and imbalanced data distributions