However, this approach necessitates either prior knowledge or learning of the graph structure, reducing its practicality. Similarly, the work in [20] assumes the existence of graph-structured datasets naturally divided into clusters, which limits its applicability to other types of datasets....
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...
论文笔记:A Survey on Graph Structure Learning: Progress and Opportunities POPO发表于图联邦学习 论文笔记:NeurIPS'21 Subgraph Federated Learning with Missing Neighbor Generation (FedSage) 天下客发表于FL-Gr... 【IJCAI 2021】Graph Filter-based Multi-view Attributed Graph Clustering 论文简读 katri...发表...
Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one
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...
GraphFL: A federated learning framework for semi-supervised node classification on graphs ZhangZ. et al. Hierarchical graph pooling with structure learning HanX. et al. G-mixup: Graph data augmentation for graph classification H. Guo, Y. Mao, Interpolating Graph Pair to Regularize Graph Classifica...
while adhering to strict privacy constraints. In such scenarios FL offers a privacy by design structure during the learning process. Still, one must be concerned about attacks that can be carried out on the final resulting model weights such as Model Inversion. In previous work, multiple factors...
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
82 Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses Yuxin Yang, Qiang Li, Jinyuan Jia, Yuan Hong, Binghui Wang 2024-12 CCS '24: Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security https://github.com/Yuxin104/Opt-GDBA https:/...
Federated Graph Learning (FGL) is tasked with training machine learning models, such as Graph Neural Networks (GNNs), for multiple clients, each with its own graph data. Existing methods usually assume that each client has both node features and graph structure of its graph data. In real-...