Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.

Overview

Awesome-Federated-Learning-on-Graph-and-GNN-papers

federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.

Federated Learning on Graphs

  1. [Arxiv 2019] Peer-to-peer federated learning on graphs. paper
  2. [NeurIPS Workshop 2019] Towards Federated Graph Learning for Collaborative Financial Crimes Detection. paper
  3. [Arxiv 2021] A Graph Federated Architecture with Privacy Preserving Learning. paper
  4. [Arxiv 2021] Federated Myopic Community Detection with One-shot Communication. paper

Federated Learning on Graph Neural Networks

Survey Papers

  1. [Arxiv 2021] FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks. paper
  2. [Arxiv 2021] Federated Graph Learning -- A Position Paper. paper

Algorithm Papers

  1. [Arxiv 2020] Federated Dynamic GNN with Secure Aggregation. paper
  2. [Arxiv 2020] Privacy-Preserving Graph Neural Network for Node Classification. paper
  3. [Arxiv 2020] ASFGNN: Automated Separated-Federated Graph Neural Network. paper
  4. [Arxiv 2020] GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs. paper
  5. [Arxiv 2021] FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation. paper
  6. [ICLR-DPML 2021] FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks. paper code
  7. [Arxiv 2021] FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search. paper
  8. [CVPR 2021] Cluster-driven Graph Federated Learning over Multiple Domains. paper
  9. [Arxiv 2021] FedGL: Federated Graph Learning Framework with Global Self-Supervision. paper
  10. [AAAI 2022] SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks. paper
  11. [KDD 2021] Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling. paper code
  12. [Arxiv 2021] A Vertical Federated Learning Framework for Graph Convolutional Network. paper
  13. [NeurIPS 2021] Federated Graph Classification over Non-IID Graphs. paper
  14. [NeurIPS 2021] Subgraph Federated Learning with Missing Neighbor Generation. paper
  15. [CIKM 2021] Differentially Private Federated Knowledge Graphs Embedding. paper code
  16. [MICCAI Workshop 2021] A Federated Multigraph Integration Approach for Connectional Brain Template Learning. paper
  17. [TPDS 2021] FedGraph: Federated Graph Learning with Intelligent Sampling. paper

Federated Learning on Knowledge Graph

  1. [Arxiv 2020] FedE: Embedding Knowledge Graphs in Federated Setting. paper code
  2. [Arxiv 2020] Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty. paper
  3. [CIKM 2021] Federated Knowledge Graphs Embedding.paper
  4. [Arxiv 2021] Leveraging a Federation of Knowledge Graphs to Improve Faceted Search in Digital Libraries. paper

Private Graph Neural Networks

  1. [IEEE Big Data 2019] A Graph Neural Network Based Federated Learning Approach by Hiding Structure. paper
  2. [Arxiv 2020] Locally Private Graph Neural Networks. paper
  3. [Arxiv 2021] Privacy-Preserving Graph Convolutional Networks for Text Classification. paper
  4. [Arxiv 2021] GraphMI: Extracting Private Graph Data from Graph Neural Networks. paper
  5. [Arxiv 2021] Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learning. paper

Federated Learning: Survey

  1. [IEEE Signal Processing Magazine 2019] Federated Learning:Challenges, Methods, and Future Directions. paper
  2. [ACM TIST 2019] Federated Machine Learning Concept and Applications. paper
  3. [IEEE Communications Surveys & Tutorials 2020] Federated Learning in Mobile Edge Networks A Comprehensive Survey. paper

Graph Neural Networks: Survey

  1. [IEEE TNNLS 2020] A Comprehensive Survey on Graph Neural Networks. paper
  2. [IEEE TKDE 2020] Deep Learning on Graphs: A Survey. paper
  3. [AI Open] Graph Neural Networks: A Review of Methods and Applications. paper
  4. [ArXiv 2021] Graph Neural Networks in Network Neuroscience. paper -- GitHub repo of all reviewed papers
You might also like...
Geometric Vector Perceptron --- a rotation-equivariant GNN for learning from biomolecular structure

Geometric Vector Perceptron Code to accompany Learning from Protein Structure with Geometric Vector Perceptrons by B Jing, S Eismann, P Suriana, RJL T

Geometric Vector Perceptrons --- a rotation-equivariant GNN for learning from biomolecular structure
Geometric Vector Perceptrons --- a rotation-equivariant GNN for learning from biomolecular structure

Geometric Vector Perceptron Implementation of equivariant GVP-GNNs as described in Learning from Protein Structure with Geometric Vector Perceptrons b

Code for ICCV 2021 paper
Code for ICCV 2021 paper "Distilling Holistic Knowledge with Graph Neural Networks"

HKD Code for ICCV 2021 paper "Distilling Holistic Knowledge with Graph Neural Networks" cifia-100 result The implementation of compared methods are ba

Distance Encoding for GNN Design

Distance-encoding for GNN design This repository is the official PyTorch implementation of the DEGNN and DEAGNN framework reported in the paper: Dista

GNN-based Recommendation Benchma
GNN-based Recommendation Benchma

GRecX A Fair Benchmark for GNN-based Recommendation Preliminary Comparison DiffNet-Yelp dataset (featureless) Algo nDCG@5 nDCG@10 nDCG@15 MF 0.158707

TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A good teacher is patient and consistent by Beyer et al.

FunMatch-Distillation TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A g

[IJCAI-2021] A benchmark of data-free knowledge distillation from paper
[IJCAI-2021] A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation"

DataFree A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation" Authors: Gongfa

Source Code for our paper: Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated Recurrent Memory Network
Source Code for our paper: Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated Recurrent Memory Network

KaGRMN-DSG_ABSA This repository contains the PyTorch source Code for our paper: Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated

Official implementation of
Official implementation of "GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators" (NeurIPS 2020)

GS-WGAN This repository contains the implementation for GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators (NeurIPS

Comments
Owner
keven
keven
PyTorch implementation of Federated Learning with Non-IID Data, and federated learning algorithms, including FedAvg, FedProx.

Federated Learning with Non-IID Data This is an implementation of the following paper: Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vik

Youngjoon Lee 48 Dec 29, 2022
TianyuQi 10 Dec 11, 2022
Robbing the FED: Directly Obtaining Private Data in Federated Learning with Modified Models

Robbing the FED: Directly Obtaining Private Data in Federated Learning with Modified Models This repo contains a barebones implementation for the atta

null 16 Dec 4, 2022
[CIKM 2019] Code and dataset for "Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction"

FiGNN for CTR prediction The code and data for our paper in CIKM2019: Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Predicti

Big Data and Multi-modal Computing Group, CRIPAC 75 Dec 30, 2022
DIR-GNN - Discovering Invariant Rationales for Graph Neural Networks

DIR-GNN "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)

Ying-Xin (Shirley) Wu 70 Nov 13, 2022
TorchDistiller - a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

This project is a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

yifan liu 147 Dec 3, 2022
A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks A Research-oriented Federated Learning Library and Benchmark Platform

FedML-AI 175 Dec 1, 2022
QA-GNN: Question Answering using Language Models and Knowledge Graphs

QA-GNN: Question Answering using Language Models and Knowledge Graphs This repo provides the source code & data of our paper: QA-GNN: Reasoning with L

Michihiro Yasunaga 434 Jan 4, 2023
A graph neural network (GNN) model to predict protein-protein interactions (PPI) with no sample features

A graph neural network (GNN) model to predict protein-protein interactions (PPI) with no sample features

null 2 Jul 25, 2022
Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle

TF Watcher TF Watcher is a simple to use Python package and web app which allows you to monitor ?? your Machine Learning training or testing process o

Rishit Dagli 54 Nov 1, 2022