AI RESEARCH
Fine-Tuning Impairs the Balancedness of Foundation Models in Long-tailed Personalized Federated Learning
arXiv CS.CV
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ArXi:2605.02247v1 Announce Type: new Personalized federated learning (PFL) with foundation models has emerged as a promising paradigm enabling clients to adapt to heterogeneous data distributions. However, real-world scenarios often face the co-occurrence of non-IID data and long-tailed class distributions, presenting unique challenges that remain underexplored in