{"total":48,"rows":[{"id":"2205.07424","title":"Trustworthy Graph Neural Networks: Aspects, Methods and Trends","created":"2022-05-16","cited_by_count":89,"area":null},{"id":"2306.01603","title":"Decentralized Federated Learning: A Survey and Perspective","created":"2023-06-02","cited_by_count":86,"area":null},{"id":"2404.15381","title":"Advances and Open Challenges in Federated Foundation Models","created":"2024-04-23","cited_by_count":36,"area":null},{"id":"2308.16375","title":"A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications","created":"2023-08-31","cited_by_count":22,"area":null},{"id":"2303.06471","title":"Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review","created":"2023-03-11","cited_by_count":20,"area":null},{"id":"2410.20105","title":"FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference","created":"2024-10-26","cited_by_count":16,"area":null},{"id":"2210.17281","title":"GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers","created":"2022-10-31","cited_by_count":11,"area":null},{"id":"2307.04338","title":"Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey","created":"2023-07-10","cited_by_count":10,"area":null},{"id":"2312.09006","title":"FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning","created":"2023-12-14","cited_by_count":10,"area":null},{"id":"2206.03420","title":"An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning","created":"2022-06-07","cited_by_count":7,"area":null},{"id":"2507.02409","title":"S2FGL: Spatial Spectral Federated Graph Learning","created":"2025-07-03","cited_by_count":7,"area":null},{"id":"2507.16541","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","created":"2025-07-22","cited_by_count":7,"area":null},{"id":"2405.15458","title":"FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler","created":"2024-05-24","cited_by_count":6,"area":null},{"id":"2503.03995","title":"Subgraph Federated Learning for Local Generalization","created":"2025-03-06","cited_by_count":6,"area":null},{"id":"2303.00524","title":"Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach","created":"2023-02-28","cited_by_count":5,"area":null},{"id":"2405.17309","title":"Survey of Graph Neural Network for Internet of Things and NextG Networks","created":"2024-05-27","cited_by_count":4,"area":null},{"id":"2502.16703","title":"Subsampling Graphs with GNN Performance Guarantees","created":"2025-02-23","cited_by_count":4,"area":null},{"id":"2505.16248","title":"Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems","created":"2025-05-22","cited_by_count":4,"area":null},{"id":"2509.00402","title":"Curriculum Guided Personalized Subgraph Federated Learning","created":"2025-08-30","cited_by_count":4,"area":null},{"id":"2402.19163","title":"Decoupled Subgraph Federated Learning","created":"2024-02-29","cited_by_count":3,"area":null},{"id":"2407.15320","title":"Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence","created":"2024-07-07","cited_by_count":3,"area":null},{"id":"2410.06340","title":"FedGraph: A Research Library and Benchmark for Federated Graph Learning","created":"2024-10-08","cited_by_count":3,"area":null},{"id":"2506.18525","title":"Federated Learning from Molecules to Processes: A Perspective","created":"2025-06-23","cited_by_count":3,"area":null},{"id":"2308.15324","title":"Federated Neuro-Symbolic Learning","created":"2023-08-29","cited_by_count":2,"area":null},{"id":"2310.20552","title":"Privacy-preserving design of graph neural networks with applications to vertical federated learning","created":"2023-10-31","cited_by_count":2,"area":null},{"id":"2407.04460","title":"Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learning","created":"2024-07-05","cited_by_count":2,"area":null},{"id":"2408.07966","title":"Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization","created":"2024-08-15","cited_by_count":2,"area":null},{"id":"2511.23037","title":"Time Extrapolation with Graph Convolutional Autoencoder and Tensor Train Decomposition","created":"2025-11-28","cited_by_count":2,"area":null},{"id":"2310.02651","title":"Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learning","created":"2023-10-04","cited_by_count":1,"area":null},{"id":"2410.11290","title":"Backdoor Attack on Vertical Federated Graph Neural Network Learning","created":"2024-10-15","cited_by_count":1,"area":null},{"id":"2504.21206","title":"FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs","created":"2025-04-29","cited_by_count":1,"area":null},{"id":"2203.07802","title":"A Framework for Verifiable and Auditable Federated Anomaly Detection","created":"2022-03-15","cited_by_count":0,"area":null},{"id":"2312.03120","title":"The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning","created":"2023-12-05","cited_by_count":0,"area":null},{"id":"2408.05249","title":"Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review","created":"2024-08-08","cited_by_count":0,"area":null},{"id":"2409.14655","title":"Federated Graph Learning with Adaptive Importance-based Sampling","created":"2024-09-23","cited_by_count":0,"area":null},{"id":"2410.14010","title":"Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information","created":"2024-10-17","cited_by_count":0,"area":null},{"id":"2410.15849","title":"Focus Where It Matters: Graph Selective State Focused Attention Networks","created":"2024-10-21","cited_by_count":0,"area":null},{"id":"2410.21006","title":"A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks","created":"2024-10-28","cited_by_count":0,"area":null},{"id":"2503.12156","title":"Efficient and Privacy-Preserved Link Prediction via Condensed Graphs","created":"2025-03-15","cited_by_count":0,"area":null},{"id":"2504.11808","title":"Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphs","created":"2025-04-16","cited_by_count":0,"area":null},{"id":"2507.19964","title":"Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networks","created":"2025-07-26","cited_by_count":0,"area":null},{"id":"2512.23235","title":"FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs","created":"2025-12-29","cited_by_count":0,"area":null},{"id":"2601.17935","title":"FedGraph-VASP: Privacy-Preserving Federated Graph Learning with Post-Quantum Security for Cross-Institutional Anti-Money Laundering","created":"2026-01-25","cited_by_count":0,"area":null},{"id":"2602.00407","title":"Fed-Listing: Federated Label Distribution Inference in Graph Neural Networks","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2602.04093","title":"Federated Concept-Based Models: Interpretable models with distributed supervision","created":"2026-02-04","cited_by_count":0,"area":null},{"id":"2603.07743","title":"Hide and Find: A Distributed Adversarial Attack on Federated Graph Learning","created":"2026-03-08","cited_by_count":0,"area":null},{"id":"2605.08288","title":"UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2606.26125","title":"Geometric Fairness-Aware Routing for Federated Edge Networks","created":"2026-05-31","cited_by_count":0,"area":null}],"next_cursor":null}