【Author】
Qi, Yuanhang; Hossain, M. Shamim; Nie, Jiangtian; Li, Xuandi
【Source】FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
【Abstract】As accurate and timely traffic flow information is extremely important for traffic management, traffic flow prediction has become a vital component of intelligent transportation systems. However, existing traffic flow prediction methods based on centralized machine learning need to gather raw data for model training, which involves serious privacy exposure risks. To address these problems, federated learning that shares model updates without exchanging raw data, has recently been introduced as an efficient solution for achieving privacy protection. However, the existing federated learning frameworks are based on a centralized model coordinator that still suffers from severe security challenges, such as a single point of failure. Thereby, a consortium blockchain-based federated learning framework is proposed to enable decentralized, reliable, and secure federated learning without a centralized model coordinator. In the proposed framework, the model updates from distributed vehicles are verified by miners to prevent unreliable model updates and are then stored on the blockchain. In addition, to further protect model privacy on the blockchain, a differential privacy method with a noise-adding mechanism is applied for the blockchain-based federated learning framework. Numerical results illustrate that the proposed schemes can effectively prevent data poisoning attacks and improve the privacy protection of model updates for secure and privacy-preserving traffic flow prediction. (C) 2020 Elsevier B.V. All rights reserved.
【Keywords】Federated learning; Blockchain; Local differential privacy; Traffic flow prediction; Intelligent transportation systems
【摘要】由于准确及时的交通流信息对交通管理极为重要，因此交通流预测已成为智能交通系统的重要组成部分。然而，现有的基于集中式机器学习的交通流预测方法需要收集原始数据进行模型训练，存在严重的隐私泄露风险。为了解决这些问题，最近引入了在不交换原始数据的情况下共享模型更新的联邦学习作为实现隐私保护的有效解决方案。然而，现有的联邦学习框架基于集中式模型协调器，仍然面临着严峻的安全挑战，例如单点故障。因此，提出了一种基于联盟区块链的联邦学习框架，以在没有集中模型协调器的情况下实现去中心化、可靠和安全的联邦学习。在所提出的框架中，来自分布式车辆的模型更新由矿工验证，以防止不可靠的模型更新，然后存储在区块链上。此外，为了进一步保护区块链上的模型隐私，在基于区块链的联邦学习框架中应用了一种带有噪声添加机制的差分隐私方法。数值结果表明，所提出的方案可以有效地防止数据中毒攻击，并提高模型更新的隐私保护，以实现安全和隐私保护的交通流预测。 (C) 2020 Elsevier B.V. 保留所有权利。
【关键词】联邦学习；区块链；本地差分隐私；交通流量预测；智能交通系统
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