36 citations · 63 across the 10 of their papers we have counts for
10 papers
Towards Instance-wise Personalized Federated Learning via Semi-Implicit Bayesian Prompt Tuning
Tiandi Ye, Wenyan Liu, Kai Yao +6
Federated learning (FL) is a privacy-preserving machine learning paradigm that enables collaborative model training across multiple distributed clients without disclosing their raw…
FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models
Mingzhao Yang, Shangchao Su, Bin Li +1
In recent years, the attention towards One-Shot Federated Learning (OSFL) has been driven by its capacity to minimize communication. With the development of the diffusion model (DM…
FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients
Shangchao Su, Bin Li, Xiangyang Xue
With the increasing availability of Foundation Models, federated tuning has garnered attention in the field of federated learning, utilizing data and computation resources from mul…
One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Models
Mingzhao Yang, Shangchao Su, Bin Li +1
In recent years, One-shot Federated Learning methods based on Diffusion Models have garnered increasing attention due to their remarkable performance. However, most of these method…
Collaborative Chinese Text Recognition with Personalized Federated Learning
Shangchao Su, Haiyang Yu, Bin Li +1
In Chinese text recognition, to compensate for the insufficient local data and improve the performance of local few-shot character recognition, it is often necessary for one organi…
Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model
Mingzhao Yang, Shangchao Su, Bin Li +1
Recently, semi-supervised federated learning (semi-FL) has been proposed to handle the commonly seen real-world scenarios with labeled data on the server and unlabeled data on the…
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-…