Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing
Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing
复制标题
DOI：
10.1109/jiot.2021.3128155
复制
发表时间：
2021-10
影响因子：
10.6
通讯作者：
Qin Hu;Zhilin Wang;Minghui Xu;Xiuzhen Cheng
中科院分区：
文献类型：
--
作者：
Qin Hu;Zhilin Wang;Minghui Xu;Xiuzhen Cheng
移动人群感知(MCS)依靠海量工作人员的移动性，帮助请求者以更高的灵活性和更低的成本完成各种感知任务。然而，对于传统的MCS来说，原始数据传输对通信资源的消耗很大，对数据存储和计算能力的要求很高，这阻碍了资源有限的潜在请求者使用MCS。为了促进MCS的广泛应用，我们提出了一种新的MCS学习框架，该框架利用区块链技术和基于联合学习的边缘智能的新概念，涉及请求者、区块链、边缘服务器和移动设备四个主要实体作为工作者。尽管已经有一些关于基于区块链的MCS和基于区块链的FL的研究，但它们不能解决MCS在容纳资源受限的请求者方面的本质挑战，也不能解决请求者和工作者参与学习过程带来的隐私问题。为了填补这一空白，设计了四个主要程序，即任务发布、数据传感和提交、学习返回最终结果、支付结算和分配，以应对恶意边缘服务器和不诚实的请求者等内外部威胁带来的重大挑战。具体而言，提出了一种基于机制设计的数据提交规则，以保证移动设备的数据隐私在边缘服务器上得到真实保护；提出了基于联盟区块链的FL机制，以确保分布式学习过程的安全；并设计了一种合作强制控制策略，以向请求者收取全额费用。为了评估我们设计的方案的性能，我们进行了大量的仿真。
Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novel MCS learning framework leveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers, and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design-based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain-based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes.