Federated learning methods enable us to train machine learning models on distributed user data while preserving its privacy. However, it is not always feasible to obtain high-quality supervisory signals from users, especially for vision tasks. Unlike typical federated settings with labeled client data, we consider a more practical scenario where the distributed client data is unlabeled, and a centralized labeled dataset is available on the server. We further take the server-client and inter-client domain shifts into account and pose a domain adaptation problem with one source (centralized server data) and multiple targets (distributed client data). Within this new Federated Multi-Target Domain Adaptation (FMTDA) task, we analyze the model performance of exiting domain adaptation methods and propose an effective DualAdapt method to address the new challenges. Extensive experimental results on image classification and semantic segmentation tasks demonstrate that our method achieves high accuracy, incurs minimal communication cost, and requires low computational resources on client devices.
FedDA-TSformer: Federated Domain Adaptation with Vision TimeSformer for Left Ventricle Segmentation on Gated Myocardial Perfusion SPECT Image
Kennesaw State University（肯尼索州立大学）；Michigan Technological University（密歇根理工大学）；Xiangya Hospital, Central South University（中南大学湘雅医院）；Chang Bing Show Chwan Memorial Hospital（彰滨秀传纪念医院）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；Health Research Institute, Michigan Technological University（密歇根理工大学健康研究所）
Multi-site Organ Segmentation with Federated Partial Supervision and Site Adaptation
Beijing UCAS Space Technology Co., Ltd.（北京国科大空间技术有限公司）；University of Science and Technology of China（中国科学技术大学）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）
Med-DualLoRA: Local Adaptation of Foundation Models for 3D Cardiac MRI
Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain（巴塞罗那大学人工智能医学实验室（BCN-AIM）、数学与计算机科学系，西班牙）；Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain（加泰罗尼亚高级研究机构（ICREA），巴塞罗那，西班牙）
pFedBBN: A Personalized Federated Test-Time Adaptation with Balanced Batch Normalization for Class-Imbalanced Data
Center for Computational & Data Sciences (CCDS)（计算与数据科学中心）；Independent University（独立大学）；University of Central Florida（中央佛罗里达大学）；Curtin University（Curtin大学）；Umeå University（乌梅拉大学）
On the Effectiveness of Adaptation Strategies for VLM-Based Federated Learning in Remote Sensing
BIFOLD - Berlin Institute for the Foundations of Learning and Data（BIFOLD - 柏林学习与数据基础研究所）；Technische Universität Berlin（柏林工业大学）；National Technical University of Athens（雅典国家技术大学）；National Observatory of Athens（雅典国家天文台）；Harokopio University of Athens（哈罗科皮奥雅典大学）
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）
Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation
Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid（马德里理工大学电信工程学院）；CIBER-BBN, Instituto de Salud Carlos III（卡洛斯三世健康研究所生物工程、生物材料与纳米医学网络中心）；Departments of Radiology and Pediatrics, The George Washington University（乔治华盛顿大学放射学与儿科学系）；NVIDIA Corporation（英伟达公司）
FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning
1 Center for Computational \& Data Sciences, Independent University, Bangladesh；2 Curtin University；Center for Computational \& Data Sciences Independent University, Bangladesh；School of Electrical Engineering, Computing \& Mathematical Sciences (EECMS) Curtin University
Federated LoRA Adaptation of BiomedCLIP Across Four International Chest X-Ray Cohorts
North Carolina A&T State University（北卡罗来纳农工州立大学）；Tribhuvan University（特里布文大学）；University of Tennessee-Knoxville（田纳西大学诺克斯维尔分校）；University of South Dakota（南达科他大学）
Efficient Cross-Domain Federated Learning by MixStyle Approximation
Technical University of Appl. Sc. Würzburg-Schweinfurt（维尔茨堡-施韦因富特应用科学技术大学）；Center for Artificial Intelligence and Robotics（人工智能与机器人中心）；University of Groningen（格罗宁根大学）
