SI-ChainFL: Shapley-Incentivized Secure Federated Learning for High-Speed Rail Data Sharing
This work provides a more robust and incentivized federated learning framework for high-speed rail systems, addressing data sharing and security concerns for rail operators.
The paper addresses challenges in federated learning for high-speed rail data sharing, specifically insufficient incentives and centralized aggregation. It proposes SI-ChainFL, a framework that uses Shapley values to quantify client contributions, considering rare-event utility, data diversity, quality, and timeliness, and employs a blockchain-based consensus for decentralized aggregation. Experiments show SI-ChainFL maintains effectiveness with 90% malicious clients in PA attacks, achieving 14.12% higher accuracy than RAGA.
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