Resource Management and Optimization in Internet of Vehicles for Hierarchical Federated Learning

Due to limited network resources in internet of vehicles (IoV), vehicle's heterogenous data, communication and computing resources significantly impact the training delay and model accuracy of federated learning (FL). To enhance resource utilization in IoV and mitigate the impact of limited res...

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Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.158174-158188
Main Authors: Yuan, Tangju, Chen, Liwan, Jiang, Yutao, Chen, Honghao, Gong, Wenbin, Gu, Yu
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Base stations
Communication
Computation
Computational modeling
Data models
Data privacy
Deep learning
deep reinforcement learning
Federated learning
Heterogeneity
Internet of Vehicles
Machine learning
Markov processes
Network latency
Optimization
Resource allocation
Resource management
Resource utilization
Servers
Training
vehicle selection
Vehicles
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Summary:Due to limited network resources in internet of vehicles (IoV), vehicle's heterogenous data, communication and computing resources significantly impact the training delay and model accuracy of federated learning (FL). To enhance resource utilization in IoV and mitigate the impact of limited resources on FL, we propose a resource allocation strategy to optimize computing and communication resource distribution within IoV. Additionally, A vehicle selection strategy based on vehicle data, communication and computing resources is introduced to reduce the impact of resource and data heterogeneity on FL. To further enhance FL training efficiency, a hierarchical FL algorithm that integrates vehicle selection and resource allocation based on deep deterministic policy gradients is designed. The vehicle selection and resource allocation problems are modeled as a Markov decision process. Deep reinforcement learning is adopted to find the optimal strategy. Simulation results demonstrate that the proposed algorithm achieves rapid convergence. It significantly reduces FL training latency while ensuring training accuracy.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3486775