Graph-Based Traffic Forecasting via Communication-Efficient Federated Learning

Author:

Zhang Chenhan1,Zhang Shiyao1,Yu Shui2,Yu James J.Q.1

Affiliation:

1. Southern University of Science and Technology,Dept. of Computer Science and Engineering,Shenzhen,China

2. University of Technology Sydney,School of Computer Science,Sydney,Australia

Publisher

IEEE

Reference21 articles.

1. PMF

2. A joint learning and communications framework for federated learning over wireless networks;chen;IEEE Transactions on Wireless Communications,2020

3. Federated learning: Strategies for improving communication efficiency;kone?ny,2016

4. Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints;sattler;IEEE Transactions on Neural Networks and Learning Systems,2020

5. Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks

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1. Survey of Federated Learning Models for Spatial-Temporal Mobility Applications;ACM Transactions on Spatial Algorithms and Systems;2024-07-13

2. Towards Communication-Efficient Federated Learning Through Particle Swarm Optimization and Knowledge Distillation;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02

3. Graph convolutional neural networks for traffic forecasting and prediction: A review;AIP Conference Proceedings;2024

4. Federated Graph Neural Networks: Overview, Techniques, and Challenges;IEEE Transactions on Neural Networks and Learning Systems;2024

5. Graph Neural Network for Traffic Forecasting: The Research Progress;ISPRS International Journal of Geo-Information;2023-02-27

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