Federated Graph Learning

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2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

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FedeRICo: Federated Region-Influenced Coupling for Traffic Flow Prediction
Sep 17, 2026Fermin Orozco, Man Luo, Johan WahlströmFederated Graph Learning Spatiotemporal Forecasting
University of Exeter Exeter, United Kingdom
Structural Negative Transfer in Federated Graph Neural Networks: Diagnosis, Causal Investigation, and the Limits of Divergence-Aware Mitigation
Sep 15, 2026Chethana Prasad Kabgere, Shylaja SSGraph Neural Networks Federated Graph Learning
PES University, Bengaluru, India
From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning
Sep 2, 2026Rémi Bourgerie, Šarūnas Girdzijauskas, Viktoria FodorFederated Graph Learning Decentralized Learning
School of Electrical Engineering and Computer Science, and Digital Futures KTH Royal Institute of Technology Stockholm, Sweden
Privacy-Preserving Topology-Guided Safety for LLM-Based Multi-Agent Systems via Federated Graph Learning
Sep 2, 2026Jinxi Yu, Eric Hanchen Jiang, Levina Li +6Multi-Agent LLM Systems Federated Learning Aggregation
University of California, Los Angeles
F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting
Aug 10, 2026Jiayi Zhang, Jinfeng Xu, Hewei Wang +7Federated Learning Aggregation Federated Graph Learning
University of Nottingham · The University of Hong Kong · Carnegie Mellon University +1
Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity
Aug 1, 2026Yinlin Zhu, Di Wu, Yi Zhang +5Multimodal Federated Learning Federated Graph Learning
Sun Yat-sen University, Guangzhou, China1 · Shandong University, Weihai, China2 · Beijing Institute of Technology, Beijing, China3
MMFGU: Multimodal Federated Graph Unlearning
Jul 30, 2026Haodong Lu, Zekai Chen, Weiwei Ji +5Multimodal Federated Learning Federated Unlearning
Beijing Institute of Technology Beijing, China
FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning
Jul 30, 2026Zekai Chen, Haodong Lu, Shihao Li +5Multimodal Federated Learning Federated Continual Learning
Beijing Institute of Technology, Beijing, China
FedLSG: LLM-Enhanced Semantic Calibration for Federated Graph Backdoor Defense
Jul 22, 2026Chenyu Zhou, Yabin Peng, Wei Huang +3Graph Neural Networks Backdoor Attacks
Southeast University Purple Mountain Laboratories · Institute of AI for Industries Chinese Academy of Sciences · Purple Mountain Laboratories
Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework
Jul 17, 2026Xunkai Li, Guohao Fu, Yuming Ai +4Multimodal Federated Learning Cross-Modal Alignment
Beijing Institute of Technology, Beijing, 100811, China
FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning
Jun 30, 2026Zekai Chen, Kairui Yang, Xuaner Chen +4Multimodal Federated Learning Representation Learning
Beijing Institute of Technology, Beijing, China
Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approach
Jun 23, 2026Lianshuai Guo, Zhongzheng Yuan, Xunkai Li +2Graph Structure Learning Long-Tail Learning
Shandong University, School of Airspace Science and Engineering, Weihai 264209, China · Beijing Institute of Technology, School of Computer Science and Technology, Beijing 100081, China
Privacy-Preserving Federated Temporal Graph Learning with Digital Twin--Guided Adaptive Deception for Cyber-Resilient IoMT
Jun 19, 2026Syed Zeeshan Haider, Anwar Shah, Muneeb Arif +2Federated RL Autonomous Cyber Defense
FAST National University of Computer and Emerging Sciences, Islamabad, Pakistan
Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach
Jun 18, 2026Zhengyu Wu, Hongchao Qin, Xunkai Li +3Multimodal Federated Learning Missing-Modality Learning
Guiding Federated Graph Recommendation with LLM-encoded knowledge
Jun 13, 2026Thi Minh Chau Nguyen, Hien Trang Nguyen, Duc Anh Nguyen +3Federated Learning Aggregation Graph Representation Learning
PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning
Jun 8, 2026Zekai Chen, Miao Zhang, Jiayang Xing +4Multimodal Federated Learning Missing-Modality Learning
Beijing Institute of Technology, Beijing, 100081, China
Geometric Fairness-Aware Routing for Federated Edge Networks
May 31, 2026Ratun RahmanEdge Computing Federated Graph Learning
Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA
DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphs
May 29, 2026Ashley Hoi-Ting Au, Zikun Zhang, Ligang He +1Communication-Efficient Distributed Training Federated Graph Learning
Department of Computer Science, The University of Warwick · School of Computing and Communications, Lancaster University
Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks
May 25, 2026Zhishuai Guo, Wenhan Wu, Chen Chen +3Graph Neural Networks Communication-Efficient Distributed Training
Department of Computer Science Northern Illinois University · Department of Computer Science University of North Carolina at Charlotte · Department of Computer Science University of Central Florida +2
Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity
May 12, 2026Sirui Zhang, Haonan Wang, Xunkai Li +5Multimodal Federated Learning Missing-Modality Learning
Beijing Institute of Technology
STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning
May 12, 2026Zekai Chen, Xun Wu, Xunkai Li +3Multimodal Federated Learning Cross-Modal Representation Learning
Beijing Institute of Technology, Beijing, 100081, China · Minzu University of China, Beijing, 100081, China
FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection
May 10, 2026Yunfeng Zhao, Yixin Liu, Qingfeng Chen +3Federated Graph Learning Reconstruction-Based Anomaly Detection
Guangxi University · Griffith University
UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
May 8, 2026Shih-Yu Lai, Hirozumi Yamaguchi, Shang-Tse Chen +2Multimodal Federated Learning Federated Learning Aggregation
National Taiwan University · RIKEN-CCS · The University of Osaka +1
Federated Cross-Client Subgraph Pattern Detection
May 7, 2026Selin Ceydeli, Rui Wang, Kubilay AtasuGraph Neural Networks Federated Graph Learning
Delft University of Technology, Department of Software Technology Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands
Beyond Rigid Alignment: Graph Federated Learning via Dual Manifold Calibration
May 7, 2026Wentao Yu, Bo Han, Jie Yang +1Federated Graph Learning Manifold Learning
School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing, China · Department of Computer Science Hong Kong Baptist University Hong Kong, China · School of Automation and Intelligent Sensing Shanghai Jiao Tong University Shanghai, China
Generalized Category Discovery in Federated Graph Learning
May 5, 2026Zhongzheng Yuan, Lianshuai Guo, Xunkai Li +2Federated Graph Learning Federated Learning
Shandong University, School of Airspace Science and Engineering, Weihai 264209, China · Beijing Institute of Technology, School of Computer Science and Technology, Beijing 100081, China
Graph Federated Unlearning for Privacy Preservation
May 4, 2026Ruotong Ma, Wentao Yu, Qizhou Wang +2Federated Unlearning Federated Graph Learning
Shanghai Jiao Tong University · Nanjing University of Science and Technology · Hong Kong Baptist University China
FedIA: Importance-Aware Aggregation for Domain-Robust Federated Graph Learning
Sep 17, 2025Zhanting Zhou, Zeyu Ma, Ziqiang Zheng +1Federated Learning Aggregation Domain Generalization
University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China
FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting
Jul 24, 2025Zhongzheng Yuan, Lianshuai Guo, Xunkai Li +3Asynchronous Federated Learning Non-IID Federated Learning
Shandong University, School of Airspace Science and Engineering, Weihai 264209, China · Beijing Institute of Technology, School of Computer Science and Technology, Beijing 100081, China · Sun Yat-sen University, School of Computer Science and Engineering, Guangzhou 510275, China
Hyper-modal Imputation Diffusion Embedding with Dual-Distillation for Federated Multimodal Knowledge Graph Completion
Jun 27, 2025Ying Zhang, Yu Zhao, Xuhui Sui +5Multimodal Federated Learning Missing-Modality Learning
College of Computer Science, VCIP, DISSec, Nankai University, Tianjin, China · School of Software, Tiangong University, Tianjin, China · Shenzhen Campus of Sun Yat-sen University, Shenzhen, China +1

Sep 17, 2026, cs.AIJ/K move · Enter open · S save

FedeRICo: Federated Region-Influenced Coupling for Traffic Flow Prediction

Fermin Orozco, Man Luo, Johan Wahlström

University of Exeter Exeter, United Kingdom

Federated Graph Learning Spatiotemporal Forecasting Traffic Forecasting Spatiotemporal GNNs Heterogeneous Federated Learning

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Urban traffic forecasting often relies on information distributed across stakeholders who may be unable to share raw data due to privacy or commercial constraints, motivating federated spatial-temporal approaches. In such federated settings, each client observes traffic over a distinct sensor subgraph with its own spatial topology and temporal dynamics, leading to significant heterogeneity across clients. Existing federated spatial-temporal methods typically rely on model parameter aggregation and provide limited mechanisms for recovering spatial dependencies across client boundaries. This introduces two key limitations. Specifically, parameter aggregation across heterogeneous graph domains tends to dilute client-specific representations, while road network partitioning breaks the propagation of traffic dynamics across client boundaries. To address these challenges, we propose FedeRICo, a federated traffic forecasting framework that combines gradient-level collaboration with boundary-aware residual communication. FedeRICo employs a dual-branch forecasting architecture in which a globally guided branch captures transferable forecasting structure, while a private residual branch preserves client-specific corrections and incorporates boundary residual signals. The global branch is coordinated through gradient alignment across all clients, enabling collaborative optimisation without destructive parameter interference. To recover cross-client spatial dependencies, boundary messages are extracted through a trend-residual decomposition that suppresses periodic structure and communicates only transient spatial-temporal residual signals between physically adjacent clients. Experiments across four real-world traffic forecasting benchmarks demonstrate that FedeRICo consistently outperforms state-of-the-art federated spatial-temporal baselines while maintaining competitive training runtime.

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