论文笔记:ICML'21 SpreadGNN: Serverless Multi task Federated Learning for Graph Neural Networks 天下客 机器学习、联邦学习、图神经网络14 人赞同了该文章 前言 GNN 是分析解决图机器学习问题的首选方法,但是由于用户方的隐私问题、法规限制和商业竞争等原因,集中大量的真实世界图数据用于GNN的训练是很困难的。
原文链接:SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks: 摘要 Graph Neural Networks (GNNs) are the first choice methods for graph machine learning problems thanks to their ability to learn state-of-the-art level representations from graph-structured data. However, ...
Spreadgnn: Serverless multi-task federated learning for graph neural networks, 2021. [2] Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Koneˇcn`y, Sanjiv Kumar, and H Brendan McMahan. Adaptive federated optimization. arXiv preprint arXiv:2003.00295, 2020....
36 Federated Continual Graph Learning Yinlin Zhu, Xunkai Li, Miao Hu, Di Wu 2024-11-28 arXiv https://github.com/zyl24/FCGL_POWER https://doi.org/10.48550/arXiv.2411.18919 37 Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures Yicheng Zhang, Zhen Qi...
et al. Federated learning improves site performance in multicenter deep learning without data sharing. J. Am. Med. Inform. Assoc. 28, 1259–1264 (2021). Article Google Scholar Shen, C. et al. Multi-task federated learning for heterogeneous pancreas segmentation. In Clinical Image-Based ...
Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tasks, assuming model congruity that identical model architecture is deployed in each client. To relax this assumption and thus extend real-world applicability, we introduce a novel problem setting, Hetero-Client Federated Multi-...
Vertically federated graph neural network for privacy-preserving node classification. In: Proceedings of the International Joint Conference on Artificial Intelligence, 2022. 1959–1965 MATH Google Scholar Smith V, Chiang C K, Sanjabi M, et al. Federated multi-task learning. In: Proceedings of ...
联邦学习( Federation Learning,FL )是在隐私受限的场景中学习一个中心模型(即服务器),其中数据存储在多个设备(即客户)上。中心模型不直接获取数据,只对每个客户端本地计算的参数进行更新。这就产生了一个问题,称为统计异质,因为客户可能具有不同的数据分布(即域)。聚类可以通过识别领域来降低异构性,但是它剥夺了每...
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)。
Federated Learning is thede-facto standardfor collaborative training of machine learning models over many distributed edge devices without the need for centralization. 一鸽就几个月,实在sorry,直接进入主题。 intro 图神经网络(GNN)[17]是一种表达模型,可以将结构知识提取到具有高度代表性的嵌入中。虽然图是...