Federated Domain Generalization

Latest papers

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

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

Oct 19, 2026J/K move · Enter open · S save

Communication-Efficient Federated Domain Generalization in Edge Networks via Dynamic Prototype Regulation

Yuankun Xia, Hui Wang, Ling Yuan, Ziyu Hong+2

School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China

Federated Learning Federated Domain Generalization Non-IID Federated Learning Federated Fine-Tuning Vision Foundation Model Adaptation

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Federated learning (FL) is a distributed paradigm for Internet of Things (IoT) edge networks. However, deploying Vision-Language Models (VLMs) over wireless edge networks presents distinct challenges: large model parameters increase uplink transmission latency over bandwidth-constrained fading channels, and non-independent and identically distributed (Non-IID) data exacerbates client drift. Existing feature alignment solutions often introduce auxiliary computations that consume time slots allocated for wireless parameter transmission, increasing the likelihood of straggler dropouts under system delay constraints. To address these issues, we propose DPGR-GCAM, a communication-efficient federated domain generalization framework. By employing a dual-parameter-efficient fine-tuning (PEFT) architecture, we design a channel-aware tiered transmission mechanism that adaptively truncates payloads to maximize node survival rates under fading conditions. To mitigate client drift, we introduce the Gradient-Conflict Aware Modulation (GCAM) mechanism. GCAM operates within the low-dimensional feature manifold to dynamically resolve multi-objective optimization conflicts with minimal edge computation latency, thereby reducing computation-induced network outages. Experiments demonstrate that DPGR-GCAM improves cross-domain generalization and sustains convergence under network fading scenarios with a 10% client participation rate.