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cs.AI2025
Federated Cross-Training Learners for Robust Generalization under Data Heterogeneity
Zhuang Qi, Lei Meng, Ruohan Zhang +5
Federated learning benefits from cross-training strategies, which enables models to train on data from distinct sources to improve generalization capability. However, due to inhere…
cs.AI2024
Relation Modeling and Distillation for Learning with Noisy Labels
Xiaming Che, Junlin Zhang, Zhuang Qi +1
Learning with noisy labels has become an effective strategy for enhancing the robustness of models, which enables models to better tolerate inaccurate data. Existing methods either…