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 clients. However, existing methods face several challenges such as communication costs, data heterogeneity, and training pressure on client devices. To address these challenges, we introduce the powerful diffusion models (DM) into semi-FL and propose FedDISC, a Federated Diffusion-Inspired Semi-supervised Co-training method. Specifically, we first extract prototypes of the labeled server data and use these prototypes to predict pseudo-labels of the client data. For each category, we compute the cluster centroids and domain-specific representations to signify the semantic and stylistic information of their distributions. After adding noise, these representations are sent back to the server, which uses the pre-trained DM to generate synthetic datasets complying with the client distributions and train a global model on it. With the assistance of vast knowledge within DM, the synthetic datasets have comparable quality and diversity to the client images, subsequently enabling the training of global models that achieve performance equivalent to or even surpassing the ceiling of supervised centralized training. FedDISC works within one communication round, does not require any local training, and involves very minimal information uploading, greatly enhancing its practicality. Extensive experiments on three large-scale datasets demonstrate that FedDISC effectively addresses the semi-FL problem on non-IID clients and outperforms the compared SOTA methods. Sufficient visualization experiments also illustrate that the synthetic dataset generated by FedDISC exhibits comparable diversity and quality to the original client dataset, with a neglectable possibility of leaking privacy-sensitive information of the clients.
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）
Exploring Vacant Classes in Label-Skewed Federated Learning
University of Science and Technology of China（中国科学技术大学）；Anhui University（安徽大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Nanjing University（南京大学）
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）
Exploring the Applications of Faster R-CNN and Single-Shot Multi-box Detection in a Smart Nursery Domain
Universiti Teknologi Brunei（文莱达鲁萨兰大学）；National Institute of Information and Communications Technology（信息通信研究机构）；CLEALINKTECHNOLOGY Co., Ltd.（CLEALINK科技有限公司）
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）
