【Author】
Otoum, Safa; Al Ridhawi, Ismaeel; Mouftah, Hussein T.
【Source】2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
【Abstract】The advances in today's IoT devices and machine learning methods have given rise to the concept of Federated Learning. Through such a technique, a plethora of network devices collaboratively train and update a mutual machine learning model while protecting their individual data-sets. Federated learning proves its effectiveness in tackling communication efficiency and privacy-safeguarding issues. Moreover, blockchain was introduced to solve many network issues in regard to data privacy and network single point of failure. In this article, we introduce a solution that integrates both federated learning and blockchain to ensure both data privacy and network security. We present a framework to decentralize the mutual machine learning models on end-devices. A blockchain-based consensus solution as a second line of privacy is used to ensure trustworthy shared training on the fog. The proposed model enables on-end device machine learning without any centralized training of the data nor coordination by utilizing a consensus method in the blockchain. We evaluate and verify our proposed model through simulation to showcase the effectiveness of the adapted scheme in terms of accuracy, energy consumption, and lifetime rate, along with throughput and latency metrics. The proposed model performs with an accuracy rate of approximate to 0.97.
【Keywords】Federated learning; Blockchain; Consensus; Privacy; Vehicular; Trustworthy
【摘要】当今物联网设备和机器学习方法的进步催生了联邦学习的概念。通过这种技术，大量网络设备可以协作训练和更新相互机器学习模型，同时保护它们各自的数据集。联邦学习证明了它在解决通信效率和隐私保护问题方面的有效性。此外，引入区块链来解决有关数据隐私和网络单点故障的许多网络问题。在本文中，我们介绍了一种将联邦学习和区块链相结合的解决方案，以确保数据隐私和网络安全。我们提出了一个框架来分散终端设备上的相互机器学习模型。使用基于区块链的共识解决方案作为第二道隐私线，以确保在雾上进行可信赖的共享训练。所提出的模型无需对数据进行任何集中训练，也无需通过使用区块链中的共识方法进行协调即可实现终端设备机器学习。我们通过仿真评估和验证我们提出的模型，以展示适应方案在准确性、能耗和生命周期以及吞吐量和延迟指标方面的有效性。所提出的模型以接近 0.97 的准确率执行。
【关键词】联邦学习；区块链；共识;隐私;车载；值得信赖
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