mlsanityData to 7 Oct 2026

M3FGM:a node masking and multi-granularity message passing-based federated graph model for spatial-temporal data prediction

arXiv 2210.16193 ↗First posted 27 Oct 2022cs.LG cs.AIcited by 0

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Abstract (arXiv)

Researchers are solving the challenges of spatial-temporal prediction by combining Federated Learning (FL) and graph models with respect to the constrain of privacy and security. In order to make better use of the power of graph model, some researchs also combine split learning(SL). However, there are still several issues left unattended: 1) Clients might not be able to access the server during inference phase; 2) The graph of clients designed manually in the server model may not reveal the proper relationship between clients. This paper proposes a new GNN-oriented split federated learning method, named node bfseries Masking and bfseries Multi-granularity bfseries Message passing-based Federated Graph Model (M3FGM) for the above issues. For the first issue, the server model of M3FGM employs a MaskNode layer to simulate the case of clients being offline. We also redesign the decoder of the client model using a dual-sub-decoders structure so that each client model can use its local data to predict independently when offline. As for the second issue, a new GNN layer named Multi-Granularity Message Passing (MGMP) layer enables each client node to perceive global and local information. We conducted extensive experiments in two different scenarios on two real traffic datasets. Results show that M3FGM outperforms the baselines and variant models, achieves the best results in both datasets and scenarios.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 36 references that are in the map. First posted on arXiv 27 Oct 2022.

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24 of 36 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  5. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  6. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  7. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  8. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  9. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 679
  10. 2017Federated Multi-Task Learningcited by 669
  11. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 443
  12. 2020SplitFed: When Federated Learning Meets Split Learningcited by 308
12 more
  1. 2018Distributed learning of deep neural network over multiple agentscited by 277
  2. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 209
  3. 2022Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecastingcited by 122
  4. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
  5. 2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
  6. 2020End-to-End Evaluation of Federated Learning and Split Learning for Internet of Thingscited by 64
  7. 2021Cluster-driven Graph Federated Learning over Multiple Domainscited by 43
  8. 2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecastingcited by 36
  9. 2021GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Predictioncited by 36
  10. 2022Personalized Federated Learning With Graphcited by 29
  11. 2022Federated Learning with Heterogeneous Architectures using Graph HyperNetworkscited by 17
  12. 2022Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attentioncited by 15

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