{"total":30,"rows":[{"id":"2204.08570","title":"A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability","created":"2022-04-18","cited_by_count":118,"area":null},{"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":"2109.14236","title":"LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning","created":"2021-09-29","cited_by_count":64,"area":null},{"id":"2206.10206","title":"Personalized Subgraph Federated Learning","created":"2022-06-21","cited_by_count":54,"area":null},{"id":"2403.04468","title":"A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges","created":"2024-03-07","cited_by_count":54,"area":null},{"id":"2202.07256","title":"Federated Graph Neural Networks: Overview, Techniques and Challenges","created":"2022-02-15","cited_by_count":52,"area":null},{"id":"2301.01299","title":"Recent Advances on Federated Learning: A Systematic Survey","created":"2023-01-03","cited_by_count":41,"area":null},{"id":"2111.06061","title":"Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI","created":"2021-11-11","cited_by_count":39,"area":null},{"id":"2111.11066","title":"FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks","created":"2021-11-22","cited_by_count":32,"area":null},{"id":"2205.09702","title":"Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis","created":"2022-05-19","cited_by_count":29,"area":null},{"id":"2201.12433","title":"FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks","created":"2022-01-28","cited_by_count":24,"area":null},{"id":"2302.02573","title":"Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey","created":"2023-02-06","cited_by_count":23,"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":"2106.07976","title":"Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection","created":"2021-06-15","cited_by_count":20,"area":null},{"id":"2202.03195","title":"More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks","created":"2022-02-07","cited_by_count":15,"area":null},{"id":"2207.11812","title":"Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications","created":"2022-07-24","cited_by_count":14,"area":null},{"id":"2312.13306","title":"Towards Fair Graph Federated Learning via Incentive Mechanisms","created":"2023-12-20","cited_by_count":8,"area":null},{"id":"2401.15203","title":"FedGT: Federated Node Classification with Scalable Graph Transformer","created":"2024-01-26","cited_by_count":5,"area":null},{"id":"2306.03191","title":"Personalized Federated Domain Adaptation for Item-to-Item Recommendation","created":"2023-06-05","cited_by_count":4,"area":null},{"id":"2308.08072","title":"Decentralized Graph Neural Network for Privacy-Preserving Recommendation","created":"2023-08-15","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":"2207.11836","title":"Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning","created":"2022-07-24","cited_by_count":2,"area":null},{"id":"2210.01803","title":"Federated Graph-based Networks with Shared Embedding","created":"2022-10-03","cited_by_count":1,"area":null},{"id":"2212.12158","title":"Graph Federated Learning with Hidden Representation Sharing","created":"2022-12-23","cited_by_count":1,"area":null},{"id":"2501.12592","title":"FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Sampling","created":"2025-01-22","cited_by_count":1,"area":null},{"id":"2508.13642","title":"Personalized Subgraph Federated Learning with Sheaf Collaboration","created":"2025-08-19","cited_by_count":1,"area":null},{"id":"2506.08169","title":"Federated Learning on Stochastic Neural Networks","created":"2025-06-09","cited_by_count":0,"area":null},{"id":"2512.12022","title":"DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning","created":"2025-12-12","cited_by_count":0,"area":null},{"id":"2605.01810","title":"Federated Semi-Supervised Graph Neural Networks with Prototype-Guided Pseudo-Labeling for Privacy-Preserving Gestational Diabetes Mellitus Prediction","created":"2026-05-03","cited_by_count":0,"area":null}],"next_cursor":null}