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Mobile crowdsensing (MCS) counting on the mobility of massive workers helpsthe requestor accomplish various sensing tasks with more flexibility and lowercost. However, for the conventional MCS, the large consumption of communicationresources for raw data transmission and high requirements on data storage andcomputing capability hinder potential requestors with limited resources fromusing MCS. To facilitate the widespread application of MCS, we propose a novelMCS learning framework leveraging on blockchain technology and the new conceptof edge intelligence based on federated learning (FL), which involves fourmajor entities, including requestors, blockchain, edge servers and mobiledevices as workers. Even though there exist several studies on blockchain-basedMCS and blockchain-based FL, they cannot solve the essential challenges of MCSwith respect to accommodating resource-constrained requestors or deal with theprivacy concerns brought by the involvement of requestors and workers in thelearning process. To fill the gaps, four main procedures, i.e., taskpublication, data sensing and submission, learning to return final results, andpayment settlement and allocation, are designed to address major challengesbrought by both internal and external threats, such as malicious edge serversand dishonest requestors. Specifically, a mechanism design based datasubmission rule is proposed to guarantee the data privacy of mobile devicesbeing truthfully preserved at edge servers; consortium blockchain based FL iselaborated to secure the distributed learning process; and acooperation-enforcing control strategy is devised to elicit full payment fromthe requestor. Extensive simulations are carried out to evaluate theperformance of our designed schemes.

Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing