mlsanityData to 14 Oct 2026

FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning

arXiv 2204.05562 ↗First posted 12 Apr 2022cs.LGcited by 43

Not on the map. Why?

Abstract (arXiv)

The incredible development of federated learning (FL) has benefited various tasks in the domains of computer vision and natural language processing, and the existing frameworks such as TFF and FATE has made the deployment easy in real-world applications. However, federated graph learning (FGL), even though graph data are prevalent, has not been well supported due to its unique characteristics and requirements. The lack of FGL-related framework increases the efforts for accomplishing reproducible research and deploying in real-world applications. Motivated by such strong demand, in this paper, we first discuss the challenges in creating an easy-to-use FGL package and accordingly present our implemented package FederatedScope-GNN (FS-G), which provides (1) a unified view for modularizing and expressing FGL algorithms; (2) comprehensive DataZoo and ModelZoo for out-of-the-box FGL capability; (3) an efficient model auto-tuning component; and (4) off-the-shelf privacy attack and defense abilities. We validate the effectiveness of FS-G by conducting extensive experiments, which simultaneously gains many valuable insights about FGL for the community. Moreover, we employ FS-G to serve the FGL application in real-world E-commerce scenarios, where the attained improvements indicate great potential business benefits. We publicly release FS-G, as submodules of FederatedScope, at https://github.com/alibaba/FederatedScope to promote FGL's research and enable broad applications that would otherwise be infeasible due to the lack of a dedicated package.

Key facts. Cited by 43 papers in the map (data through 14 Oct 2026). Builds on 32 of its 46 references that are in the map. First posted on arXiv 12 Apr 2022.

Builds on

32 of 46 references.

References · 32

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2017Inductive Representation Learning on Large Graphscited by 6,320
  5. 2018How Powerful are Graph Neural Networks?cited by 4,393
  6. 2019Advances and Open Problems in Federated Learningcited by 3,046
  7. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  8. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  9. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  10. 2019Towards Federated Learning at Scale: System Designcited by 832
  11. 2018LEAF: A Benchmark for Federated Settingscited by 792
  12. 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 699
20 more
  1. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  2. 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 595
  3. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  4. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  5. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  6. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  7. 2018Contextual Stochastic Block Modelscited by 134
  8. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  9. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 124
  10. 2021Federated Graph Classification over Non-IID Graphscited by 114
  11. 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 85
  12. 2021Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharingcited by 58
  13. 2020IBM Federated Learning: an Enterprise Framework White Paper V0.1cited by 57
  14. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  15. 2021Differentially Private Federated Knowledge Graphs Embeddingcited by 45
  16. 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
  17. 2021FedGL: Federated Graph Learning Framework with Global Self-Supervisioncited by 34
  18. 2019Towards Federated Graph Learning for Collaborative Financial Crimes Detectioncited by 33
  19. 2020Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacycited by 22
  20. 2021FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspectivecited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 7; 2023: 8; 2024: 14; 2025: 8; 2026: 6.

43 papers.

Sort:

By area:

  1. 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 149
  2. 2022Personalized Subgraph Federated Learningcited by 54
  3. 2023FedMultimodal: A Benchmark For Multimodal Federated Learningcited by 51
  4. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  5. 2024FedGTA: Topology-aware Averaging for Federated Graph Learningcited by 44
  6. 2023Efficient Personalized Federated Learning via Sparse Model-Adaptationcited by 38
  7. 2024AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneitycited by 25
  8. 2023FS-Real: Towards Real-World Cross-Device Federated Learningcited by 22
  9. 2024On the Convergence of Zeroth-Order Federated Tuning for Large Language Modelscited by 19
  10. 2023Towards Data-centric Graph Machine Learning: Review and Outlookcited by 15
  11. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  12. 2024Deep Efficient Private Neighbor Generation for Subgraph Federated Learningcited by 14
31 more
  1. 2025Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglementcited by 14
  2. 2022FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphscited by 12
  3. 2022GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Serverscited by 11
  4. 2023Privacy-Preserving Graph Machine Learning from Data to Computation: A Surveycited by 10
  5. 2025FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modelingcited by 8
  6. 2022FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimizationcited by 6
  7. 2024Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networkscited by 6
  8. 2025Federated Learning for Medical Image Classification: A Comprehensive Benchmarkcited by 6
  9. 2024FedGT: Federated Node Classification with Scalable Graph Transformercited by 5
  10. 2024Federated Continual Graph Learningcited by 5
  11. 2023Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Networkcited by 4
  12. 2024HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learningcited by 4
  13. 2024FedSheafHN: Personalized Federated Learning on Graph-structured Datacited by 3
  14. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 3
  15. 2024FedGraph: A Research Library and Benchmark for Federated Graph Learningcited by 3
  16. 2025Tree-based Models for Vertical Federated Learning: A Surveycited by 3
  17. 2022Privacy-Preserved Neural Graph Similarity Learningcited by 2
  18. 2024FedRSU: Federated Learning for Scene Flow Estimation on Roadside Unitscited by 2
  19. 2025FedAGHN: Personalized Federated Learning with Attentive Graph HyperNetworkscited by 2
  20. 2024FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Frameworkcited by 1
  21. 2025FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Samplingcited by 1
  22. 2026Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Frameworkcited by 1
  23. 2026Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneitycited by 1
  24. 2023Scalable Neural Network Training over Distributed Graphscited by 0
  25. 2024SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generationcited by 0
  26. 2025FLIP: Towards Comprehensive and Reliable Evaluation of Federated Prompt Learningcited by 0
  27. 2025Optimizing Federated Learning using Remote Embeddings for Graph Neural Networkscited by 0
  28. 2026DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphscited by 0
  29. 2026Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approachcited by 0
  30. 2026FedLSG: LLM-Enhanced Semantic Calibration for Federated Graph Backdoor Defensecited by 0
  31. 2026Coupling Perception and Reasoning in Federated Multimodal Graph Foundation Modelscited by 0