SI-ChainFL: Shapley-Incentivized Secure Federated Learning for High-Speed Rail Data Sharing
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866910046145740800 |
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| author | Zhao, Mingjie Dai, Cheng Chen, Fei Chen, Xin Ota, Kaoru Dong, Mianxiong Guo, Bing |
| author_facet | Zhao, Mingjie Dai, Cheng Chen, Fei Chen, Xin Ota, Kaoru Dong, Mianxiong Guo, Bing |
| contents | In high-speed rail (HSR) systems, federated learning (FL) enables cross-departmental flow prediction without sharing raw data. However, existing schemes suffer from two key limitations: (1) insufficient incentives, leading to free-riding and model poisoning; and (2) centralized aggregation, which introduces a single point of failure. We propose a secure and efficient framework SI-ChainFL that addresses these issues by combining contribution-aware incentives with decentralized aggregation. First, we quantify client contributions using a Shapley value metric that jointly considers rare-event utility, data diversity, data quality, and timeliness. To reduce computational overhead, we further develop a rare positive driven client clustering strategy to accelerate Shapley estimation. Moreover, we design a blockchain-based consensus protocol for decentralized aggregation, where aggregation eligibility is tied to Shapley incentives. This design motivates clients to submit high-quality updates and enables efficient and secure global aggregation. Experiments on MNIST, CIFAR 10 and CIFAR 100, and a HSR flow dataset show that SI ChainFL remains effective under 90% malicious clients in PA attacks, achieving 14.12% higher accuracy than RAGA. Theoretical analysis further guarantees an upper bound on performance |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_07992 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SI-ChainFL: Shapley-Incentivized Secure Federated Learning for High-Speed Rail Data Sharing Zhao, Mingjie Dai, Cheng Chen, Fei Chen, Xin Ota, Kaoru Dong, Mianxiong Guo, Bing Distributed, Parallel, and Cluster Computing 68M14 I.2.11 In high-speed rail (HSR) systems, federated learning (FL) enables cross-departmental flow prediction without sharing raw data. However, existing schemes suffer from two key limitations: (1) insufficient incentives, leading to free-riding and model poisoning; and (2) centralized aggregation, which introduces a single point of failure. We propose a secure and efficient framework SI-ChainFL that addresses these issues by combining contribution-aware incentives with decentralized aggregation. First, we quantify client contributions using a Shapley value metric that jointly considers rare-event utility, data diversity, data quality, and timeliness. To reduce computational overhead, we further develop a rare positive driven client clustering strategy to accelerate Shapley estimation. Moreover, we design a blockchain-based consensus protocol for decentralized aggregation, where aggregation eligibility is tied to Shapley incentives. This design motivates clients to submit high-quality updates and enables efficient and secure global aggregation. Experiments on MNIST, CIFAR 10 and CIFAR 100, and a HSR flow dataset show that SI ChainFL remains effective under 90% malicious clients in PA attacks, achieving 14.12% higher accuracy than RAGA. Theoretical analysis further guarantees an upper bound on performance |
| title | SI-ChainFL: Shapley-Incentivized Secure Federated Learning for High-Speed Rail Data Sharing |
| topic | Distributed, Parallel, and Cluster Computing 68M14 I.2.11 |
| url | https://arxiv.org/abs/2603.07992 |