Toward Robust and Generalizable Federated Graph Neural Networks for Decentralized Spatial-Temporal Data Modeling-Reference-Cited by-同舟云学术

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Toward Robust and Generalizable Federated Graph Neural Networks for Decentralized Spatial-Temporal Data Modeling

Published:2024-06 Issue:3 Volume:21 Page:2637-2650
ISSN:1932-4537
Container-title:IEEE Transactions on Network and Service Management
language:
Short-container-title:IEEE Trans. Netw. Serv. Manage.

Author:

Tian Yuxing¹,Liu Lei¹^ORCID,Feng Jie²^ORCID,Pei Qingqi²^ORCID,Chen Chen²^ORCID,Du Jun³^ORCID,Wu Celimuge⁴

Affiliation:

1. Guangzhou Institute of Technology, Xidian University, Guangzhou, China

2. State Key Laboratory of integrated Service Networks and the School of Telecommunications Engineer, Xidian University, Xi’an, China

3. Department of Electronic Engineering, Tsinghua University, Beijing, China

4. Meta-Networking Research Center, The University of Electro-Communications, Tokyo, Japan

Funder

National Natural Science Foundation of China

Key Research and Development Programs of Shaanxi

Innovation Capability Support Program of Shaanxi

Key Project on Artificial Intelligence of Xi’an Science and Technology Plan

JST ASPIRE

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4275028/10599562/10496499.pdf?arnumber=10496499

Reference36 articles.

1. Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehicular Edge Computing

2. Graph Convolutional Neural Networks for Web-Scale Recommender Systems

3. Graph Neural Networks for Recommender System

4. Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network

5. Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting

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