SI-ChainFL: Shapley-Incentivized Secure Federated Learning for High-Speed Rail Data Sharing

2026-03-09

AI Summary

This study addresses critical challenges in federated learning for high-speed railway (HSR) systems, including free-riding and model poisoning due to insufficient client incentives, as well as single-point failure risks inherent in centralized aggregation. To mitigate these issues, the authors propose a Shapley value-based contribution measurement mechanism that integrates utility from rare events, data diversity, quality, and timeliness. Building upon this incentive-aware metric, they design a blockchain-based consensus protocol to enable decentralized, secure, and efficient model aggregation. Experimental results demonstrate that the proposed approach remains robust even under severe poisoning attacks involving up to 90% malicious clients. On an HSR dataset, it achieves a 14.12% higher accuracy compared to RAGA, and its effectiveness is further validated on standard benchmarks including MNIST and CIFAR.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Mechanism Design
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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

Problem

• federated learning
• incentive mechanism
• decentralized aggregation
• model poisoning
• single point of failure

Innovation

• Shapley value
• blockchain-based consensus
• federated learning
• client clustering
• decentralized aggregation