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 FL(graph-structured),其中图内 FL(intra-graph FL)又分为横向和纵向 FGL。对于每一种类型的 FGL,本文对其表述...
然而,不同的分布式存储的图数据,即使来自同一领域或同一数据集,在图结构和节点特征方面都是 no-IID(Feature distribution skew, Label distribution skew 和 Quantity skew)。 基于此,本文提出了一个图聚类联合学习(graph clustered federated learning,GCFL)框架,该框架基于 GNN 的梯度动态地找到局部系统的簇,并从理论...
Federated Graph Semantic and Structural LearningWenke Huang, Guancheng Wan, Mang Ye, Bo DuAbstractFederated graph learning collaboratively learns a global graph neural network with distributed graphs, where the non-independent and identically distributed property is one of the major challenge. Most ...
By organizing network traffic information in chronological order and constructing a graph structure based on log density, enhances the accuracy of network attack detection. The introduction of an attention mechanism and the construction of a Federated Graph Attention Network (FedGAT) model are used to...
To simulate and handle this unique challenge, we introduce the concept of structure Non-iid split and then present a new paradigm called Adaptive Federated Graph Learning (AdaFGL), a decoupled two-step personalized approach. To begin with, AdaFGL employs standard multi-client federated ...
Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of primary concern. Meanwhile, neural architecture search has become very popular in deep learning for automatically tuning the archi...
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitatio
This distribution is shown in the graph provided in Figure 5 below (in the pie chart, total will not add up to 100% since one study may contribute to more than one area). Figure 5. Count per contribution area. 3.2.2. Classification by the Aggregation Approach On the other hand, ...
Moreover, the edge-based IoT converges with intelligence modeling to utilize three basic elements of blockchain technology including graph structure, tree, and chain. Most application services such as healthcare, transportation, entertainment, etc., rely on a cloud-centric machine learning model to ...
Graph Federated Learning: Working on the ubiquitous graph data, Graph Federated Learning aims to exploit isolated sub-graph data to learn a global model, and has attracted increasing popularity. Recommendation: As a number of laws and regulations go into effect all over the world, more and more...