AI RESEARCH
FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning
arXiv CS.LG
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ArXi:2604.19729v1 Announce Type: new Personalized Federated Learning (PFL) aims to learn multiple task-specific models rather than a single global model across heterogeneous data distributions. Existing PFL approaches typically rely on iterative optimization-such as model update trajectories-to cluster users that need to accomplish the same tasks together. However, these learning-dynamics-based methods are inherently vulnerable to low-quality data and noisy labels, as corrupted updates distort clustering decisions and degrade personalization performance.