Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

【Author】 Zhao, Yang; Zhao, Jun; Jiang, Linshan; Tan, Rui; Niyato, Dusit; Li, Zengxiang; Lyu, Lingjuan; Liu, Yingbo

【Source】IEEE INTERNET OF THINGS JOURNAL

【Abstract】Home appliance manufacturers strive to obtain feedback from users to improve their products and services to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging a reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data. Then, manufacturers can predict customers' requirements and consumption behaviors in the future. The working flow of the system includes two stages: in the first stage, customers train the initial model provided by the manufacturer using both the mobile phone and the mobile-edge computing (MEC) server. Customers collect data from various home appliances using phones, and then they download and train the initial model with their local data. After deriving local models, customers sign on their models and send them to the blockchain. In case customers or manufacturers are malicious, we use the blockchain to replace the centralized aggregator in the traditional FL system. Since records on the blockchain are untampered, malicious customers or manufacturers' activities are traceable. In the second stage, manufacturers select customers or organizations as miners for calculating the averaged model using received models from customers. By the end of the crowdsourcing task, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. To protect customers' privacy and improve the test accuracy, we enforce differential privacy (DP) on the extracted features and propose a new normalization technique. We experimentally demonstrate that our normalization technique outperforms batch normalization when features are under DP protection. In addition, to attract more customers to participate in the crowdsourcing FL task, we design an incentive mechanism to award participants.

【Keywords】Blockchain; Computational modeling; Crowdsourcing; Differential privacy; Home appliances; Blockchain; crowdsourcing; differential privacy (DP); federated learning (FL); Internet of Things (IoT); mobile-edge computing (MEC)

【标题】物联网设备的基于隐私保护的基于区块链的联邦学习

【摘要】家电制造商努力获得用户的反馈，以改进他们的产品和服务，以构建智能家居系统。为了帮助制造商开发智能家居系统，我们设计了一个联邦学习（FL）系统，利用信誉机制帮助家电制造商根据客户数据训练机器学习模型。然后，制造商可以预测客户未来的需求和消费行为。系统的工作流程包括两个阶段：第一阶段，客户使用手机和移动边缘计算（MEC）服务器训练制造商提供的初始模型。客户使用手机从各种家用电器中收集数据，然后下载并使用本地数据训练初始模型。导出本地模型后，客户签署他们的模型并将其发送到区块链。如果客户或制造商有恶意，我们使用区块链代替传统 FL 系统中的中心化聚合器。由于区块链上的记录未被篡改，恶意客户或制造商的活动是可追溯的。在第二阶段，制造商选择客户或组织作为矿工，使用从客户那里收到的模型计算平均模型。在众包任务结束时，其中一名被选为临时领导者的矿工将模型上传到区块链。为了保护客户隐私并提高测试准确性，我们对提取的特征实施差分隐私（DP），并提出了一种新的归一化技术。我们通过实验证明，当特征受到 DP 保护时，我们的归一化技术优于批量归一化。此外，为了吸引更多的客户参与众包 FL 任务，我们设计了激励机制来奖励参与者。

【关键词】区块链；计算建模；众包；差分隐私；家用电器;区块链；众包；差分隐私（DP）；联邦学习（FL）；物联网（IoT）；移动边缘计算 (MEC)

【发表时间】2021

【收录时间】2022-07-06

【文献类型】Article

【论文大主题】区块链联邦学习

【论文小主题】联邦学习为主体

【影响因子】10.238

【翻译者】石东瑛