2026
Fine-Tuning Impairs the Balancedness of Foundation Models in Long-tailed Personalized Federated Learning
CVPR 2026
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