mlsanityData to 7 Oct 2026

Graph Federated Learning with Hidden Representation Sharing

arXiv 2212.12158 ↗First posted 23 Dec 2022cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

Learning on Graphs (LoG) is widely used in multi-client systems when each client has insufficient local data, and multiple clients have to share their raw data to learn a model of good quality. One scenario is to recommend items to clients with limited historical data and sharing similar preferences with other clients in a social network. On the other hand, due to the increasing demands for the protection of clients' data privacy, Federated Learning (FL) has been widely adopted: FL requires models to be trained in a multi-client system and restricts sharing of raw data among clients. The underlying potential data-sharing conflict between LoG and FL is under-explored and how to benefit from both sides is a promising problem. In this work, we first formulate the Graph Federated Learning (GFL) problem that unifies LoG and FL in multi-client systems and then propose sharing hidden representation instead of the raw data of neighbors to protect data privacy as a solution. To overcome the biased gradient problem in GFL, we provide a gradient estimation method and its convergence analysis under the non-convex objective. In experiments, we evaluate our method in classification tasks on graphs. Our experiment shows a good match between our theory and the practice.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 35 of its 47 references that are in the map. First posted on arXiv 23 Dec 2022.

Builds on

35 of 47 references.

References · 35

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  6. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  7. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  8. 2019Deep Leakage from Gradientscited by 1,004
  9. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  10. 2019Graph Neural Networks for Social Recommendationcited by 711
  11. 2017Federated Multi-Task Learningcited by 669
  12. 2020Personalized Federated Learning with Moreau Envelopescited by 612
23 more
  1. 2018Local SGD Converges Fast and Communicates Littlecited by 595
  2. 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 592
  3. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  4. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  5. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  6. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  7. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 277
  8. 2020Federated Learning of a Mixture of Global and Local Modelscited by 208
  9. 2019On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimizationcited by 178
  10. 2020A Semi-supervised Graph Attentive Network for Financial Fraud Detectioncited by 150
  11. 2018Contextual Stochastic Block Modelscited by 134
  12. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  13. 2021Federated Graph Classification over Non-IID Graphscited by 113
  14. 2020Heterogeneous Graph Neural Networks for Malicious Account Detectioncited by 95
  15. 2019Peer-to-peer Federated Learning on Graphscited by 85
  16. 2021Global Context Enhanced Graph Neural Networks for Session-based Recommendationcited by 81
  17. 2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
  18. 2020GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphscited by 64
  19. 2019Graph Neural Networks for Modelling Traffic Participant Interactioncited by 42
  20. 2021Federated Graph Learning -- A Position Papercited by 34
  21. 2020Graph Enhanced Representation Learning for News Recommendationcited by 30
  22. 2021SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networkscited by 30
  23. 2021A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularizationcited by 27

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Laplacian Kernelized Banditcited by 0