36 citations · 63 across the 10 of their papers we have counts for
4 papers · 1 filter
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning
Shangchao Su, Mingzhao Yang, Bin Li +1
Federated learning (FL) enables multiple clients to collaboratively train a global model without disclosing their data. Previous researches often require training the complete mode…
Domain Discrepancy Aware Distillation for Model Aggregation in Federated Learning
Shangchao Su, Bin Li, Xiangyang Xue
Knowledge distillation has recently become popular as a method of model aggregation on the server for federated learning. It is generally assumed that there are abundant public unl…
Cross-domain Federated Object Detection
Shangchao Su, Bin Li, Chengzhi Zhang +2
Detection models trained by one party (including server) may face severe performance degradation when distributed to other users (clients). Federated learning can enable multi-part…
One-shot Federated Learning without Server-side Training
Shangchao Su, Bin Li, Xiangyang Xue
Federated Learning (FL) has recently made significant progress as a new machine learning paradigm for privacy protection. Due to the high communication cost of traditional FL, one-…