【Author】
Qu, Youyang; Gao, Longxiang; Luan, Tom H.; Xiang, Yong; Yu, Shui; Li, Bai; Zheng, Gavin
【Source】IEEE INTERNET OF THINGS JOURNAL
【Abstract】As the extension of cloud computing and a foundation of IoT, fog computing is experiencing fast prosperity because of its potential to mitigate some troublesome issues, such as network congestion, latency, and local autonomy. However, privacy issues and the subsequent inefficiency are dragging down the performances of fog computing. The majority of existing works hardly consider a reasonable balance between them while suffering from poisoning attacks. To address the aforementioned issues, we propose a novel blockchain-enabled federated learning (FL-Block) scheme to close the gap. FL-Block allows local learning updates of end devices exchanges with a blockchain-based global learning model, which is verified by miners. Built upon this, FL-Block enables the autonomous machine learning without any centralized authority to maintain the global model and coordinates by using a Proof-of-Work consensus mechanism of the blockchain. Furthermore, we analyze the latency performance of FL-Block and further derive the optimal block generation rate by taking communication, consensus delays, and computation cost into consideration. Extensive evaluation results show the superior performances of FL-Block from the aspects of privacy protection, efficiency, and resistance to the poisoning attack.
【Keywords】Privacy; Edge computing; Data privacy; Internet of Things; Computational modeling; Data models; Blockchain; federated learning; fog computing; privacy protection
【标题】在雾计算中使用基于区块链的联邦学习来分散隐私
【摘要】作为云计算的延伸和物联网的基础，雾计算正在快速繁荣，因为它具有缓解网络拥塞、延迟和本地自治等一些麻烦问题的潜力。然而，隐私问题和随之而来的低效率正在拖累雾计算的性能。大多数现有作品在遭受中毒攻击的同时几乎没有考虑它们之间的合理平衡。为了解决上述问题，我们提出了一种新颖的支持区块链的联邦学习（FL-Block）方案来缩小差距。 FL-Block 允许使用基于区块链的全局学习模型对终端设备进行本地学习更新，该模型由矿工验证。在此基础上，FL-Block 通过使用区块链的工作量证明共识机制，实现了无需任何集中权限的自主机器学习来维护全局模型和坐标。此外，我们分析了 FL-Block 的延迟性能，并通过考虑通信、共识延迟和计算成本进一步推导出最佳块生成率。广泛的评估结果表明，FL-Block 从隐私保护、效率和抗中毒攻击等方面都具有优越的性能。
【关键词】隐私;边缘计算；数据隐私；物联网;计算建模；数据模型；区块链；联邦学习；雾计算；隐私保护
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