FedPGRL: Prototype-Guided Refinement for Noisy Long-Tailed Federated Learning

Federated Learning (FL) under label noise and long-tailed class distributions poses a fundamental challenge, as biased local supervision and skewed data jointly distort global optimization. This issue is especially pronounced when noisy annotations disproportionately affect rare classes, amplifying representation collapse across clients. We propose Federated Learning with Prototype-Guided Refinement Learning (FedPGRL), a unified framework built on CLIP2FL that addresses this coupling through prototype-driven sample refinement. FedPGRL leverages class prototypes as stable semantic anchors to guide three refinement stages: Prototypical Distance Selection (PDS), Prototypes Contrast Refinement (PCR), and Multi-Prototypical Mask Refinement (MPMR), which collectively generate soft labels and suppress noise while preserving minority-class structure. We further introduce Class Prototypical Regularization (CPR) to explicitly encourage inter-class separation and intra-class compactness during federated optimization. Extensive experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet validate that FedPGRL consistently improves convergence stability and generalization under both noisy and imbalanced FL settings.