Federated Graph Learning

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Overview

Federated graph learning studies how organizations or devices can train graph models together when their nodes, edges, and features are distributed and cannot be pooled. Common approaches adapt graph neural networks through parameter aggregation, while prototype alignment, representation exchange, and personalized or asynchronous updates address differences in client data and graph structure. Methods are evaluated on settings such as citation networks, traffic forecasting, recommendation, fraud detection, and multimodal graphs, with attention to privacy, communication cost, and robustness. A central challenge is that collaboration can cause negative transfer when clients’ structures or semantics differ, so systems must balance useful sharing with local adaptation and reliable privacy protections.

Latest papers

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46 papers1 paper in the last four weeks, down 87% on the four weeks before. 0.0% of all new papers.

  1. Pricing Cross-Bank Context in Federated Anti-Money Laundering

    Oct 26, 2026Lidia Losavio, Dario Fenoglio, Martin Gjoreski +1Anti-Money Laundering DetectionFederated Graph Learning

    Università della Svizzera italiana (USI), Lugano, Switzerland

  2. Federated Community Detection in Bipartite Networks

    Oct 19, 2026Wanjie WangGraph ClusteringFederated Graph Learning

    Department of Statistics and Data Science, National University of Singapore

  3. Coupling Perception and Reasoning in Federated Multimodal Graph Foundation Models

    Sep 24, 2026Zekai Chen, Xun Wu, Hailin Zhang +7Multimodal Federated LearningGraph Foundation Models

    Beijing Institute of Technology · Shandong University · Institute of Information Engineering +1

  4. FedeRICo: Federated Region-Influenced Coupling for Traffic Flow Prediction

    Sep 17, 2026Fermin Orozco, Man Luo, Johan WahlströmFederated Graph LearningSpatiotemporal Forecasting

    University of Exeter Exeter, United Kingdom

  5. 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 NetworksFederated Graph Learning

    PES University, Bengaluru, India

  6. Structural Entropy-Driven Graph Diffusion Generation for One-Shot Federated Graph Learning

    Sep 6, 2026Shutong Zheng, Lele Fu, Sheng Huang +2Federated Graph LearningNon-IID Federated Learning

    Sun Yat-sen University Guangzhou, China · Nanyang Technological University Singapore

  7. Rethinking One-Shot Federated Graph Learning: Training-Free Statistical Estimation

    Sep 5, 2026Shutong Zheng, Sijia ChenShrinkage EstimationFederated Graph Learning

    Sun Yat-sen University · The Hong Kong University of Science and Technology (Guangzhou)

  8. PACE: Propagation-Aware Collaborative Correction for One-Shot Personalized Federated Graph Learning

    Sep 4, 2026Ruizhe Huang, Chengran Li, Xiaochuan ShiFederated Graph LearningFederated Learning

    School of Cyber Science and Engineering, Wuhan University

  9. From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning

    Sep 2, 2026Rémi Bourgerie, Šarūnas Girdzijauskas, Viktoria FodorFederated Graph LearningDecentralized Learning

    School of Electrical Engineering and Computer Science, and Digital Futures KTH Royal Institute of Technology Stockholm, Sweden

  10. 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 SystemsFederated Learning Aggregation

    University of California, Los Angeles

  11. Conditional GraphGANFed: Optimizing Graph-Structured Molecule Generation in Federated Generative Adversarial Networks

    Aug 24, 2026Daniel Manu, Abee AlazzwiMolecular GenerationGenerative Adversarial Networks

    SECNet Lab., Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA

  12. FlatLand: Personalized Graph Federated Learning via Tailored Lorentz Space

    Aug 21, 2026Jiahong Liu, Ram Samarth B B, Xinyu Fu +4Federated Graph LearningFederated Learning

    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China

  13. F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting

    Aug 10, 2026Jiayi Zhang, Jinfeng Xu, Hewei Wang +7Federated Learning AggregationFederated Graph Learning

    University of Nottingham · The University of Hong Kong · Carnegie Mellon University +1

  14. Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity

    Aug 1, 2026Yinlin Zhu, Di Wu, Yi Zhang +5Multimodal Federated LearningFederated Graph Learning

    Sun Yat-sen University, Guangzhou, China1 · Shandong University, Weihai, China2 · Beijing Institute of Technology, Beijing, China3

  15. MMFGU: Multimodal Federated Graph Unlearning

    Jul 30, 2026Haodong Lu, Zekai Chen, Weiwei Ji +5Multimodal Federated LearningFederated Unlearning

    Beijing Institute of Technology Beijing, China

  16. FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning

    Jul 30, 2026Zekai Chen, Haodong Lu, Shihao Li +5Multimodal Federated LearningFederated Continual Learning

    Beijing Institute of Technology, Beijing, China

  17. FedLSG: LLM-Enhanced Semantic Calibration for Federated Graph Backdoor Defense

    Jul 22, 2026Chenyu Zhou, Yabin Peng, Wei Huang +3Graph Neural NetworksBackdoor Attacks

    Southeast University Purple Mountain Laboratories · Institute of AI for Industries Chinese Academy of Sciences · Purple Mountain Laboratories

  18. Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework

    Jul 17, 2026Xunkai Li, Guohao Fu, Yuming Ai +4Multimodal Federated LearningCross-Modal Alignment

    Beijing Institute of Technology, Beijing, 100811, China

  19. FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning

    Jun 30, 2026Zekai Chen, Kairui Yang, Xuaner Chen +4Multimodal Federated LearningRepresentation Learning

    Beijing Institute of Technology, Beijing, China

  20. Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approach

    Jun 23, 2026Lianshuai Guo, Zhongzheng Yuan, Xunkai Li +2Graph Structure LearningLong-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

  21. 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 RLAutonomous Cyber Defense

    FAST National University of Computer and Emerging Sciences, Islamabad, Pakistan

  22. Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach

    Jun 18, 2026Zhengyu Wu, Hongchao Qin, Xunkai Li +3Multimodal Federated LearningMissing-Modality Learning

  23. Guiding Federated Graph Recommendation with LLM-encoded knowledge

    Jun 13, 2026Thi Minh Chau Nguyen, Hien Trang Nguyen, Duc Anh Nguyen +3Federated Learning AggregationGraph Representation Learning

  24. PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning

    Jun 8, 2026Zekai Chen, Miao Zhang, Jiayang Xing +4Multimodal Federated LearningMissing-Modality Learning

    Beijing Institute of Technology, Beijing, 100081, China

  25. Geometric Fairness-Aware Routing for Federated Edge Networks

    May 31, 2026Ratun RahmanEdge ComputingFederated Graph Learning

    Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA

  26. DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphs

    May 29, 2026Ashley Hoi-Ting Au, Zikun Zhang, Ligang He +1Communication-Efficient Distributed TrainingFederated Graph Learning

    Department of Computer Science, The University of Warwick · School of Computing and Communications, Lancaster University

  27. Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks

    May 25, 2026Zhishuai Guo, Wenhan Wu, Chen Chen +3Graph Neural NetworksCommunication-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

  28. Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity

    May 12, 2026Sirui Zhang, Haonan Wang, Xunkai Li +5Multimodal Federated LearningMissing-Modality Learning

    Beijing Institute of Technology

  29. STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning

    May 12, 2026Zekai Chen, Xun Wu, Xunkai Li +3Multimodal Federated LearningCross-Modal Representation Learning

    Beijing Institute of Technology, Beijing, 100081, China · Minzu University of China, Beijing, 100081, China

  30. FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection

    May 10, 2026Yunfeng Zhao, Yixin Liu, Qingfeng Chen +3Federated Graph LearningReconstruction-Based Anomaly Detection

    Guangxi University · Griffith University