3 papers
cs.LG2025
FEDEXCHANGE: Bridging the Domain Gap in Federated Object Detection for Free
Haolin Yuan, Jingtao Li, Weiming Zhuang +2
Federated Object Detection (FOD) enables clients to collaboratively train a global object detection model without accessing their local data from diverse domains. However, signific…
cs.CV2025
UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models
Yimu Wang, Weiming Zhuang, Chen Chen +3
In the era of deep learning, the increasing number of pre-trained models available online presents a wealth of knowledge. These models, developed with diverse architectures and tra…
cs.LG2025
Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation
Guangyu Sun, Jingtao Li, Weiming Zhuang +2
Foundation models (FMs) exhibit remarkable generalization but require adaptation to downstream tasks, particularly in privacy-sensitive applications. Due to data privacy regulation…