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), several methods employ the DM for OSFL, utilizing model parameters, image features, or textual prompts as mediums to transfer the local client knowledge to the server. However, these mediums often require public datasets or the uniform feature extractor, significantly limiting their practicality. In this paper, we propose FedDEO, a Description-Enhanced One-Shot Federated Learning Method with DMs, offering a novel exploration of utilizing the DM in OSFL. The core idea of our method involves training local descriptions on the clients, serving as the medium to transfer the knowledge of the distributed clients to the server. Firstly, we train local descriptions on the client data to capture the characteristics of client distributions, which are then uploaded to the server. On the server, the descriptions are used as conditions to guide the DM in generating synthetic datasets that comply with the distributions of various clients, enabling the training of the aggregated model. Theoretical analyses and sufficient quantitation and visualization experiments on three large-scale real-world datasets demonstrate that through the training of local descriptions, the server is capable of generating synthetic datasets with high quality and diversity. Consequently, with advantages in communication and privacy protection, the aggregated model outperforms compared FL or diffusion-based OSFL methods and, on some clients, outperforms the performance ceiling of centralized training.
One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Models
Fudan University（复旦大学）；School of Computer Science, Fudan University（复旦大学计算机科学学院）；Shanghai Key Laboratory of Intelligent Information Processing（上海市智能信息处理重点实验室）
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models
Ludwig Maximilian University of Munich（慕尼黑大学）；Siemens Technology（西门子技术公司）；University of Oxford（牛津大学）；Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）
Global Prompt Refinement with Non-Interfering Attention Masking for One-Shot Federated Learning
School of Software, Shandong University（山东大学软件学院）；AIM Lab, Faculty of Engineering, Monash University（莫纳什大学工程学院AIM实验室）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Department of Electrical and Computer Engineering, University of British Columbia（不列颠哥伦比亚大学电气与计算机工程系）；Vector Institute, Canada（加拿大向量研究所）
Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation
Hong Kong Baptist University（香港浸会大学）；Narayana Superspeciality Hospital（纳拉亚纳超级专科医院）；National Institute of Technology Durgapur（杜尔加布尔国家理工学院）；Indian Institute of Technology（印度理工学院）
A New One-Shot Federated Learning Framework for Medical Imaging Classification with Feature-Guided Rectified Flow and Knowledge Distillation
Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology（广东超高清沉浸媒体技术省重点实验室）；Shenzhen Graduate School, Peking University（北京大学深圳研究生院）
Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation
Rutgers University（罗格斯大学）；Sichuan University West China Hospital（四川大学华西医院）；Walmart Global Tech（沃尔玛全球科技）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；Tongren Hospital（同仁医院）；Fudan University（复旦大学）；Zhongshan Hospital（中山医院）；SenseTime Research（商汤科技研究院）
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data
Institute of Artificial Intelligence（人工智能研究院）；School of Informatics, Xiamen University（厦门大学信息学院）；East China Normal University（华东师范大学）；Chongqing Institute of East China Normal University（华东师范大学重庆研究院）
Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机科学与信息工程学院）
