Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913450211409920 |
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| author | Chen, Weiliang Jia, Li Zhou, Yang Ren, Qianqian |
| author_facet | Chen, Weiliang Jia, Li Zhou, Yang Ren, Qianqian |
| contents | Federated learning combined with blockchain empowers secure data sharing in autonomous driving applications. Nevertheless, with the increasing granularity and complexity of vehicle-generated data, the lack of data quality audits raises concerns about multi-party mistrust in trajectory prediction tasks. In response, this paper proposes an asynchronous federated learning data sharing method based on an interpretable reputation quantization mechanism utilizing graph neural network tools. Data providers share data structures under differential privacy constraints to ensure security while reducing redundant data. We implement deep reinforcement learning to categorize vehicles by reputation level, which optimizes the aggregation efficiency of federated learning. Experimental results demonstrate that the proposed data sharing scheme not only reinforces the security of the trajectory prediction task but also enhances prediction accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19428 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain Chen, Weiliang Jia, Li Zhou, Yang Ren, Qianqian Machine Learning Cryptography and Security Computer Vision and Pattern Recognition Federated learning combined with blockchain empowers secure data sharing in autonomous driving applications. Nevertheless, with the increasing granularity and complexity of vehicle-generated data, the lack of data quality audits raises concerns about multi-party mistrust in trajectory prediction tasks. In response, this paper proposes an asynchronous federated learning data sharing method based on an interpretable reputation quantization mechanism utilizing graph neural network tools. Data providers share data structures under differential privacy constraints to ensure security while reducing redundant data. We implement deep reinforcement learning to categorize vehicles by reputation level, which optimizes the aggregation efficiency of federated learning. Experimental results demonstrate that the proposed data sharing scheme not only reinforces the security of the trajectory prediction task but also enhances prediction accuracy. |
| title | Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain |
| topic | Machine Learning Cryptography and Security Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.19428 |