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
Owner
keven
keven
(CVPR 2022) A minimalistic mapless end-to-end stack for joint perception, prediction, planning and control for self driving.

LAV Learning from All Vehicles Dian Chen, Philipp Krähenbühl CVPR 2022 (also arXiV 2203.11934) This repo contains code for paper Learning from all veh

Dian Chen 300 Dec 15, 2022
SmartSim Infrastructure Library.

Home Install Documentation Slack Invite Cray Labs SmartSim SmartSim makes it easier to use common Machine Learning (ML) libraries like PyTorch and Ten

Cray Labs 139 Jan 01, 2023
Spatial Single-Cell Analysis Toolkit

Single-Cell Image Analysis Package Scimap is a scalable toolkit for analyzing spatial molecular data. The underlying framework is generalizable to spa

Laboratory of Systems Pharmacology @ Harvard 30 Nov 08, 2022
Import Python modules from dicts and JSON formatted documents.

Paker Paker is module for importing Python packages/modules from dictionaries and JSON formatted documents. It was inspired by httpimporter. Important

Wojciech Wentland 1 Sep 07, 2022
Code for the paper Open Sesame: Getting Inside BERT's Linguistic Knowledge.

Open Sesame This repository contains the code for the paper Open Sesame: Getting Inside BERT's Linguistic Knowledge. Credits We built the project on t

9 Jul 24, 2022
Traductor de lengua de señas al español basado en Python con Opencv y MedaiPipe

Traductor de señas Traductor de lengua de señas al español basado en Python con Opencv y MedaiPipe Requerimientos 🔧 Python 3.8 o inferior para evitar

Jahaziel Hernandez Hoyos 3 Nov 12, 2022
This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?”

This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?” Usage To replicate our results in Secti

Albert Webson 64 Dec 11, 2022
Meta Learning Backpropagation And Improving It (VSML)

Meta Learning Backpropagation And Improving It (VSML) This is research code for the NeurIPS 2021 publication Kirsch & Schmidhuber 2021. Many concepts

Louis Kirsch 22 Dec 21, 2022
The implementation for the SportsCap (IJCV 2021)

SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos ProjectPage | Paper | Video | Dataset (Part01

Chen Xin 79 Dec 16, 2022
Dynamic View Synthesis from Dynamic Monocular Video

Dynamic View Synthesis from Dynamic Monocular Video Project Website | Video | Paper Dynamic View Synthesis from Dynamic Monocular Video Chen Gao, Ayus

Chen Gao 139 Dec 28, 2022
[ICCV 2021 Oral] Deep Evidential Action Recognition

DEAR (Deep Evidential Action Recognition) Project | Paper & Supp Wentao Bao, Qi Yu, Yu Kong International Conference on Computer Vision (ICCV Oral), 2

Wentao Bao 80 Jan 03, 2023
Official implementation of "MetaSDF: Meta-learning Signed Distance Functions"

MetaSDF: Meta-learning Signed Distance Functions Project Page | Paper | Data Vincent Sitzmann*, Eric Ryan Chan*, Richard Tucker, Noah Snavely Gordon W

Vincent Sitzmann 100 Jan 01, 2023
Log4j JNDI inj. vuln scanner

Log-4-JAM - Log 4 Just Another Mess Log4j JNDI inj. vuln scanner Requirements pip3 install requests_toolbelt Usage # make sure target list has http/ht

Ashish Kunwar 66 Nov 09, 2022
PyTorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision.

PyTorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision @misc{CV2018, author = {Donny You ( Donny You 40 Sep 14, 2022

Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation.

Understanding Minimum Bayes Risk Decoding This repo provides code and documentation for the following paper: Müller and Sennrich (2021): Understanding

ZurichNLP 13 May 01, 2022
Official implementations of PSENet, PAN and PAN++.

News (2021/11/03) Paddle implementation of PAN, see Paddle-PANet. Thanks @simplify23. (2021/04/08) PSENet and PAN are included in MMOCR. Introduction

395 Dec 14, 2022
General neural ODE and DAE modules for power system dynamic modeling.

Py_PSNODE General neural ODE and DAE modules for power system dynamic modeling. The PyTorch-based ODE solver is developed based on torchdiffeq. Sample

14 Dec 31, 2022
PyTorch implementation for Graph Contrastive Learning with Augmentations

Graph Contrastive Learning with Augmentations PyTorch implementation for Graph Contrastive Learning with Augmentations [poster] [appendix] Yuning You*

Shen Lab at Texas A&M University 382 Dec 15, 2022
Image morphing without reference points by applying warp maps and optimizing over them.

Differentiable Morphing Image morphing without reference points by applying warp maps and optimizing over them. Differentiable Morphing is machine lea

Alex K 380 Dec 19, 2022
Adversarial vulnerability of powerful near out-of-distribution detection

Adversarial vulnerability of powerful near out-of-distribution detection by Stanislav Fort In this repository we're collecting replications for the ke

Stanislav Fort 9 Aug 30, 2022