Federated Domain Generalization

Latest papers

13 papers4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Rethinking Model Aggregation for Federated Learning with Domain Skew: A Knowledge-Preserving Perspective
Nov 1, 2026Haotian Wu, Xu Ji, Qiheng Li +2Federated Learning Federated Learning Aggregation
Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China · Guangdong Key Laboratory of Industrial Control System Security, Guangzhou 510555, China
Domain-Aware Hierarchical Federated Learning for UAV Swarms Across Heterogeneous Environments
Nov 1, 2026Taha Mahmood, Mudassar Shakeel, Ahmed Naeem +3Hierarchical Federated Learning Heterogeneous Federated Learning
School of Electrical Engineering and Computer Science, National University of Sciences and Technology, 44000, Islamabad, Pakistan. · College of Computing, University Mohammed VI Polytechnic, Benguerir 43150, Morocco
FedUDG: Federated Domain Generalization with Unlabeled Clients
Nov 1, 2026Junyu Ren, Wensheng Gan, Philip S. YuFederated Domain Generalization Federated Learning
College of Cyber Security, Jinan University, Guangzhou 510632, China. · Department of Computer Science, University of Illinois Chicago, Chicago, USA.
Communication-Efficient Federated Domain Generalization in Edge Networks via Dynamic Prototype Regulation
Oct 19, 2026Yuankun Xia, Hui Wang, Ling Yuan +3Federated Learning Federated Domain Generalization
School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
FedSAP: Federated Learning with Structured Adaptive Partitioning for Multi-Domain Heterogeneous Edge Devices
Oct 1, 2026Wentao Yue, Tianyou Lai, Hongji Li +2Non-IID Federated Learning Federated Domain Generalization
FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift
Jun 20, 2026Kaijie Chen, Alex Johnson, Maria Garcia +2Distribution Shift Federated Domain Generalization
Mindlab
Mitigating Stethoscope-Induced Shortcuts in Respiratory Sound Classification under Federated Domain Generalization with Causality-Inspired Interventions
May 28, 2026Heejoon Koo, Yoon Tae Kim, Miika Toikkanen +1OOD Generalization Federated Domain Generalization
University of Illinois Urbana-Champaign, USA · RSC LAB, MODULABS, Korea · Wonkwang University, Korea
FedStain: Modeling Higher-Order Stain Statistics for Federated Domain Generalization in Computational Pathology
May 14, 2026Fengyi Zhang, Junya Zhang, Wenzhuo SunComputational Pathology Federated Domain Generalization
School of Electronic Science and Technology, Hainan University, Haikou, China, 570228 · School of Computer Science and Technology, Xidian University, Xi’an, China, 710126 · Xiangjiang College of Elite Engineers, Hunan University, Changsha, China, 410082
CO-EVO: Co-evolving Semantic Anchoring and Style Diversification for Federated DG-ReID
Apr 29, 2026Fengchun Zhang, Qiang Ma, Liuyu Xiang +3Cross-Domain Generalization Federated Domain Generalization
School of Information and Software Engineering, University of Electronic Science and Technology of China · 2QiYuan Lab · School of Artificial Intelligence, Beijing University of Posts and Telecommunications +1
FedBPrompt: Federated Domain Generalization Person Re-Identification via Body Distribution Aware Visual Prompts
Mar 13, 2026Xin Xu, Weilong Li, Wei Liu +5Federated Domain Generalization Person Re-Identification
School of Computer Science and Technology, Wuhan University of Science and Technology, China · Nanyang Technological University, Singapore · NERC for Multimedia Software, School of Computer Science, Wuhan University, China +3
FedARKS: Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration for Person Re-identification
Mar 6, 2026Xin Xu, Binchang Ma, Zhixi Yu +1Federated Domain Generalization Person Re-Identification
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065 China
Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization
Feb 24, 2026Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen +1Cross-Modality Medical Image Segmentation Data Augmentation
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway. · Central Staff, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
FedHUG: Federated Heterogeneous Unsupervised Generalization for Remote Physiological Measurements
Oct 14, 2025Xiao Yang, Dengbo He, Duoxun Tang +2Federated Domain Generalization Federated Learning
Systems Hub, the Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

Nov 1, 2026J/K move · Enter open · S save

Rethinking Model Aggregation for Federated Learning with Domain Skew: A Knowledge-Preserving Perspective

Haotian Wu, Xu Ji, Qiheng Li, Li Xiong+1

Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China · Guangdong Key Laboratory of Industrial Control System Security, Guangzhou 510555, China

Federated Learning Federated Learning Aggregation Federated Domain Generalization

Open paper AskPDF arXiv

Domain skew challenges federated learning by requiring a shared model to accommodate distinct visual distributions. Domain-specific experts can capture these distributions separately, creating an opportunity to integrate their representations in a unified predictor. We propose Federated Knowledge-Preserving Learning (FKPL), a two-stage framework that combines domain-oriented expert training with prototype-based classifier calibration. First, a one-shot subspace-based clustering procedure groups clients for intra-cluster training. The resulting expert encoders are concatenated and frozen, preserving each learned feature mapping during global integration. Second, clients interpolate their concatenated features with class-matched domain prototypes and upload the resulting virtual features. The server trains a global linear classifier on these features, concentrating classifier optimization at the server after a single feature upload. FKPL achieves average accuracies of 73.01%, 67.00%, and 64.51% on Office-Caltech-10, PACS, and a four-domain subset of DomainNet, respectively. On DomainNet, its cross-domain standard deviation is 7.50 percentage points, the lowest among the evaluated methods. With the same concatenated extractor, prototype-based calibration improves average accuracy over federated classifier training by 9.99 and 14.94 percentage points on Office-Caltech-10 and PACS. These results identify classifier calibration as an effective way to combine preserved domain representations into a global predictor.