3 papers
cs.CV2025
Global Prompt Refinement with Non-Interfering Attention Masking for One-Shot Federated Learning
Zhuang Qi, Pan Yu, Lei Meng +4
Federated Prompt Learning (FPL) enables communication-efficient adaptation by tuning lightweight prompts on top of frozen pre-trained models. Existing FPL methods typically rely on…
cs.CV2025
Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization
Zhuang Qi, Sijin Zhou, Lei Meng +3
Attribute bias in federated learning (FL) typically leads local models to optimize inconsistently due to the learning of non-causal associations, resulting degraded performance. Ex…
cs.CV2025
Federated Out-of-Distribution Generalization: A Causal Augmentation View
Runhui Zhang, Sijin Zhou, Zhuang Qi
Federated learning aims to collaboratively model by integrating multi-source information to obtain a model that can generalize across all client data. Existing methods often levera…