前言本文将基于图的联邦学习称为 federated graph learning(FGL)。FGL 最近受到了越来越多的关注,为了更好的定义 FGL,本文提出了四种类型的 FGL:图间 FL(intergraph FL)、图内 FL(intra-graph)和图结构 F…
graph-structured FL 图结构联邦的考虑了不同客户端之间的拓扑结构,即基于客户端的拓扑关系来使用 GNN 来聚合本地模型。 在这样的设置下,客户端持有图数据,全局模型执行任何类型的任务,目标函数与 FL 中的相同。图结构联邦可以被认为是一种特殊的联合优化方法,因为 GNN 用于提取客户端之间的固有信息以改进 FL。它...
At the same time, federated learning obeys the laws and regulations and ensures data security and data privacy. In this paper, we provide a comprehensive survey of existing works for federated learning. First, we propose a functional architecture of federated learning systems and a taxonomy of ...
Federated Graph Learning - A Position Paper 6. System Design Platform Papers Affiliations Tensorflow-Federated Towards Federated Learning at Scale: System Design Google PySyft A generic framework for privacy preserving deep learning OpenMined FedML FedML: A Research Library and Benchmark for Federated Ma...
Federated Graph Learning -- A Position Paper SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling A Vertical Federated Learning Framework for Graph Convolutional Network Federated Graph Classification over Non...
In this paper, we propose FedHGCN as a novel federated graph classification framework. Through the FedHGCN, each client can more effectively learn about deep hierarchical structure information, increasing the accuracy of graph classification. We employ the node selection and the hyperbolic aggregation...
Instead, the central focus of this paper is on the FCMs distributed training without an initial model. The main contributions of this paper are three-fold: 1. A privacy-preserving machine learning approach for FCMs. The authors design a training scheme for collaborative FCM training that ...
Lingjuan Lyu 3, Yongfeng Huang 1✉ & Xing Xie 2 Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates in each iteration of model learning rather than the ...
Federated Graph Learning -- A Position Paper @POPO 论文笔记 / @天下客 论文笔记 这篇文章在分类法方面提出了四种类型的 FGL:图间 FL(intergraph FL)、图内 FL(intra-graph)和图结构 FL(graph-structured),其中图内 FL(intra-graph FL)又分为横向和纵向 FGL。 Federated Graph Neural Networks: Overview...
此处的分类方法,对照”Federated Graph Learning -- A Position Paper“一文:1.1类似图结构联邦;2.1类似图间联邦;2.2类似横向图内联邦;2.3类似纵向图内联邦。 3. Data Owners Related by a Graph 数据所有者通过图关联 文章假设“数据所有者通过图关联”设定下,数据持有者的本地数据分布是non-IID的(通常情况下)。