mlsanityData to 7 Oct 2026

FedGraph: A Research Library and Benchmark for Federated Graph Learning

arXiv 2410.06340 ↗First posted 8 Oct 2024cs.LGcited by 3

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Abstract (arXiv)

Federated graph learning is an emerging field with significant practical challenges. While algorithms have been proposed to improve the accuracy of training graph neural networks, such as node classification on federated graphs, the system performance is often overlooked, despite it is crucial for real-world deployment. To bridge this gap, we introduce FedGraph, a research library designed for practical distributed training and comprehensive benchmarking of FGL algorithms. FedGraph supports a range of state-of-the-art graph learning methods and includes a monitoring class that evaluates system performance, with a particular focus on communication and computation costs during training. Unlike existing federated learning platforms, FedGraph natively integrates homomorphic encryption to enhance privacy preservation and supports scalable deployment across multiple physical machines with system-level performance evaluation to guide the system design of future algorithms. To enhance efficiency and privacy, we propose a low-rank communication scheme for algorithms like FedGCN that require pre-training communication, accelerating both the pre-training and training phases. Extensive experiments benchmark FGL algorithms on three major graph learning tasks and demonstrate FedGraph as the first efficient FGL framework to support encrypted low-rank communication and scale to graphs with 100 million nodes.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 8 Oct 2024.

Builds on

19 of 35 references.

References · 19

  1. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  2. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  4. 2018Federated Learning with Non-IID Datacited by 1,099
  5. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  6. 2021FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learningcited by 140
  7. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 135
  8. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  9. 2021Federated Graph Classification over Non-IID Graphscited by 113
  10. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  11. 2022BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Samplingcited by 43
  12. 2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
7 more
  1. 2023FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning Systemcited by 38
  2. 2019Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Predictioncited by 30
  3. 2022FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networkscited by 24
  4. 2021A Vertical Federated Learning Framework for Graph Convolutional Networkcited by 20
  5. 2021STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networkscited by 14
  6. 2024Deep Efficient Private Neighbor Generation for Subgraph Federated Learningcited by 14
  7. 2022FedRule: Federated Rule Recommendation System with Graph Neural Networkscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learningcited by 11
  2. 2025FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcastingcited by 0
  3. 2026Structural Negative Transfer in Federated Graph Neural Networks: Diagnosis, Causal Investigation, and the Limits of Divergence-Aware Mitigationcited by 0