mlsanityData to 7 Oct 2026

FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs

arXiv 2208.13685 ↗First posted 29 Aug 2022cs.LG cs.CRcited by 12

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Abstract (arXiv)

As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e.g., Graph Neural Networks (GNNs). However, in some practical scenarios, graph data are stored separately in multiple distributed parties, which may not be directly shared due to conflicts of interest. Hence, federated graph neural networks are proposed to address such data silo problems while preserving the privacy of each party (or client). Nevertheless, different graph data distributions among various parties, which is known as the statistical heterogeneity, may degrade the performance of naive federated learning algorithms like FedAvg. In this paper, we propose FedEgo, a federated graph learning framework based on ego-graphs to tackle the challenges above, where each client will train their local models while also contributing to the training of a global model. FedEgo applies GraphSAGE over ego-graphs to make full use of the structure information and utilizes Mixup for privacy concerns. To deal with the statistical heterogeneity, we integrate personalization into learning and propose an adaptive mixing coefficient strategy that enables clients to achieve their optimal personalization. Extensive experimental results and in-depth analysis demonstrate the effectiveness of FedEgo.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 24 of its 33 references that are in the map. First posted on arXiv 29 Aug 2022.

Builds on

24 of 33 references.

References · 24

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2018Federated Learning with Non-IID Datacited by 1,099
  6. 2017Federated Multi-Task Learningcited by 669
  7. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  8. 2019Federated Learning with Personalization Layerscited by 533
  9. 2020Adaptive Personalized Federated Learningcited by 353
  10. 2017Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Rankingcited by 332
  11. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  12. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
12 more
  1. 2020Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networkscited by 253
  2. 2020A Note on Over-Smoothing for Graph Neural Networkscited by 245
  3. 2020Federated Learning of a Mixture of Global and Local Modelscited by 208
  4. 2018Federated Meta-Learning with Fast Convergence and Efficient Communicationcited by 154
  5. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  6. 2021Federated Graph Classification over Non-IID Graphscited by 113
  7. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
  8. 2020Transfer Learning of Graph Neural Networks with Ego-graph Information Maximizationcited by 93
  9. 2020GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphscited by 64
  10. 2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
  11. 2021FedGL: Federated Graph Learning Framework with Global Self-Supervisioncited by 34
  12. 2022A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacycited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 7; 2026: 3.

12 papers.

  1. 2025Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglementcited by 14
  2. 2025FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modelingcited by 8
  3. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  4. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  5. 2025VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learningcited by 1
  6. 2025Personalized Subgraph Federated Learning with Differentiable Auxiliary Projectionscited by 1
  7. 2026Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneitycited by 1
  8. 2024Federated Graph Learning with Adaptive Importance-based Samplingcited by 0
  9. 2025Optimizing Federated Learning using Remote Embeddings for Graph Neural Networkscited by 0
  10. 2025Sharpness-aware Federated Graph Learningcited by 0
  11. 2026DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphscited by 0
  12. 2026PACE: Propagation-Aware Collaborative Correction for One-Shot Personalized Federated Graph Learningcited by 0