no code implementations • 15 Nov 2023 • Mingzhao Yang, Shangchao Su, Bin Li, xiangyang xue
Leveraging the extensive knowledge stored in the pre-trained diffusion model, the synthetic datasets can assist us in surpassing the knowledge limitations of the client samples, resulting in aggregation models that even outperform the performance ceiling of centralized training in some cases, which is convincingly demonstrated in the sufficient quantification and visualization experiments conducted on three large-scale multi-domain image datasets.
no code implementations • 6 May 2023 • Mingzhao Yang, Shangchao Su, Bin Li, xiangyang xue
Specifically, we first extract prototypes from the labeled data on the server and send them to the clients.
1 code implementation • 15 Nov 2022 • Shangchao Su, Mingzhao Yang, Bin Li, xiangyang xue
In this paper, we propose a federated adaptive prompt tuning algorithm, FedAPT, for multi-domain collaborative image classification with powerful foundation models, like CLIP.
no code implementations • 30 Jun 2022 • Shangchao Su, Bin Li, Chengzhi Zhang, Mingzhao Yang, xiangyang xue
Federated learning can enable multi-party collaborative learning without leaking client data.