[Paper] FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning

Summary

This paper introduces FB-NLL, a feature-based method designed to tackle the issue of noisy labels in Personalized Federated Learning (PFL). PFL aims to train multiple task-specific models across diverse data, but current methods are highly susceptible to low-quality data and noisy labels. These imperfections corrupt user clustering decisions and significantly degrade overall model performance, a problem FB-NLL seeks to mitigate.

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