Federated Graph Learning Resources

Federated Learning

• The Federated Learning Portal

  • Trustworthy Federated Ubiquitous Learning (TrustFUL) Research Lab

• Introduction

  • • Meeting_20211126_FedAvg

  • New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership

  • 李新春 联邦学习介绍

• Survey

  • Federated Machine Learning Concept and Applications

  • A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

  • Federated Learning：Challenges, Methods, and Future Directions

  • Federated Learning in Mobile Edge Networks: A Comprehensive Survey

  • Federated Learning for Healthcare Informatics

  • Advances and Open Problems in Federated Learning

  • Threats to Federated Learning: A Survey

  • Privacy and Robustness in Federated Learning: Attacks and Defenses

  • A Survey on Federated Learning: The Journey from Centralized to Distributed On-Site Learning and Beyond

  • From Distributed Machine Learning to Federated Learning: A Survey

  • Federated Learning for Smart Healthcare: A Survey

  • Robust and Privacy-Preserving Collaborative Learning: A Comprehensive Survey

• Non-IID

  • Federated Learning on Non-IID Data Silos: An Experimental Study

    • 论文笔记：arXiv’21 Federated Learning on Non-IID Data Silos: An Experimental Study

  • Ensemble Federated Adversarial Training with Non-IID data

  • 联邦学习数据异构问题的方法，收敛性以及展望

  • References on Federated Learning，联邦学习参考文献

  • Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives

  • Privacy and Robustness in Federated Learning: Attacks and Defenses

  • Towards Personalized Federated Learning

  • Threats to Federated Learning: A Survey

FL & GNN

• Material

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

  • 论文清单 联邦学习与图神经网络 21年·上

  • 论文清单 联邦学习与图神经网络 21年·下

  • 专栏: 基于GNN的联邦学习

  • 文章速览 | 联邦学习 x 图与图网络（2022.09.06）

  • Awesome-Federated-Learning-on-Graph-and-Tabular-Data

GNN

• Material

  • 李沐：零基础多图详解图神经网络（GNN/GCN）【论文精读】

    • A Gentle Introduction to Graph Neural Networks

    • Understanding Convolutions on Graphs

    • 如何理解 Graph Convolutional Network（GCN）？ - 陀飞轮的回答 - 知乎

  • Graph neural networks: A review of methods and applications

  • Github Awesome-GNN-Research

  • Github Awesome-Graph-GNN-Recommendation

  • Github GNN based Recommender Systems

  • 图神经网络实战专栏目录

  • 一文读懂图卷积GCN

• Code

  • Graph library – PyG、GraphGallery

  • Graph library – DIG、AutoGL、CogDL

  • GraphGallery：几行代码玩转图神经网络

  • PyG | 支持异构图、集成GraphGym，超好用的图神经网络库PyG更新2.0版本

  • PyG | 图神经网络之神器——PyTorch Geometric 上手 & 实战

  • PyG | PyTorch Geometric（一）：数据加载

  • PyG | PyTorch Geometric（二）：模型搭建

  • PyG | 图神经网络库PyTorch geometric（PYG）零基础上手教程

  • pytorch_geometric：PyG内置数据集节点分类训练并用GNNexplainer解释

  • DGL | 关系图卷积网络

• Trustworthy GNN

  • Robustness

    • 图的鲁棒性优质论文推荐

    • NIPS’20 | 用信息瓶颈增强图模型的鲁棒性

    • Graph Structure Learning for Robust Graph Neural Networks

      • 《Graph Structure Learning for Robust Graph Neural Networks》的阅读笔记

  • Privacy

    • 基于 GNN 的隐私计算（差分隐私）Review（一）

    • 基于 GNN 的隐私计算（联邦学习）Review（二）

    • 基于 GNN 的隐私计算（联邦学习）Review（三）

    • 论文笔记：Arxiv 2021 Releasing Graph Neural Networks with Differential Privacy Guarantees

    • 论文笔记：Arxiv 2020 When Differential Privacy Meets Graph Neural Networks

    • 论文笔记：CCS 2021 Locally Private Graph Neural Networks

    • 「ICML 2021」联邦学习相关论文导读(待更新)

  • Interpretability & Explainability

    • awesome-graph-explainability-papers

    • Awesome Explainable Graph Reasoning

    • ICML 2021|基于子图结构的GNN解释模型

    • [万字长文]图神经网络的解释性问题综述

    • KDD’21 | 如何评估图网络的解释性模型？

  • Others

    • [CaGCN] Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration

      • [北邮 GAMMA Lab 公众号] NeurIPS 2021| 基于置信度校正可信图神经网络

      • Github Code CaGCN

    • 图神经网络最新研究进展与应用：「扩展性」、「鲁棒性」、「表达能力」

• Heterogeneous GNN

  • Graph Neural Networks Foundations, Frontiers, and Applications

    • Chapter 16 Heterogeneous Graph Neural Networks

  • 2019 SIGKDD | Heterogeneous Graph Neural Network

  • DGL更新报告：异构图神经网络

  • 异构图神经网络入门笔记(1)：从R-GCN到HetG

  • 异构图神经网络入门笔记(2)：CompGCN

  • 异质图神经网络最新进展

  • Jhy1993/Representation-Learning-on-Heterogeneous-Graph

Knowledge

• Federated Knowledge Distillation

• 联邦学习里面的知识蒸馏技术 （一）

• 联邦学习与知识联邦

• 中国联邦学习「五个大型项目」——学习方向参考

• Enhancing Recommendation Diversity using Determinantal Point Processes on Knowledge Graphs

Recommendation Systems

• Federated Recommendation Systems

  • 231/291

• NeurIPS 2019回顾来了！联邦学习+推荐场景，微众银行首创业界联邦推荐技术

• 在推荐系统中，我还有隐私吗？联邦学习：你可以有

• FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems

• Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System

• Robust Federated Recommendation System

• FedRec: Federated Recommendation With Explicit Feedback

• Federated Social Recommendation with Graph Neural Network

• Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

  • 2021图神经网络用于推荐系统的最新综述

  • 4.4.1 GNN for Diversity

Others

社区发现算法——louvain完全指南（更新于2021-08-29）