Federated Domain Generalization

Latest papers

9 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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

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

FedSAP: Federated Learning with Structured Adaptive Partitioning for Multi-Domain Heterogeneous Edge Devices

Wentao Yue, Tianyou Lai, Hongji Li, Qingyu Mao+1

Non-IID Federated Learning Federated Domain Generalization Structured Pruning Federated Learning Heterogeneous Federated Learning

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Federated learning (FL) on heterogeneous edge devices must jointly accommodate unequal resource budgets and domain-shifted local data. Existing resource-adaptive methods decide how much of a model each client trains but not where retained capacity should reside or how it should be shared, whereas federated domain-generalization methods usually assume a shared full architecture. Uniform compression can therefore discard high-utility channels, and a single aggregation path can mix transferable features with domain-sensitive updates. We propose FedSAP, a domain-aware heterogeneous FL framework that casts structured pruning as budget-constrained tri-state channel allocation. FedSAP converts each keep ratio into non-uniform layer budgets, assigns stable channels to a Global pool, useful domain-sensitive channels to pseudo-domain-specific Private pools, and low-utility channels to a Dropped state. This partition lets broadly useful features benefit from cross-client pooling while isolating domain-sensitive updates from incompatible clients. Domain-Guided Assignment infers pseudo-domains from shallow-gradient similarity, while Type-Matched Aggregation restricts each channel to its intended sharing scope. Across three random seeds, FedSAP reaches 76.00% and 72.67% mean global accuracy on Digits and Office-Caltech, exceeding the strongest baseline by 1.70 and 4.92 percentage points while supporting client pruning ratios of up to 80% across heterogeneous clients.