  Mobile crowdsensing (MCS) is an emerging sensing data collection pattern with
scalability, low deployment cost, and distributed characteristics. Traditional
MCS systems suffer from privacy concerns and fair reward distribution.
Moreover, existing privacy-preserving MCS solutions usually focus on the
privacy protection of data collection rather than that of data processing. To
tackle faced problems of MCS, in this paper, we integrate federated learning
(FL) into MCS and propose a privacy-preserving MCS system, called
\textsc{CrowdFL}. Specifically, in order to protect privacy, participants
locally process sensing data via federated learning and only upload encrypted
training models. Particularly, a privacy-preserving federated averaging
algorithm is proposed to average encrypted training models. To reduce
computation and communication overhead of restraining dropped participants,
discard and retransmission strategies are designed. Besides, a
privacy-preserving posted pricing incentive mechanism is designed, which tries
to break the dilemma of privacy protection and data evaluation. Theoretical
analysis and experimental evaluation on a practical MCS application demonstrate
the proposed \textsc{CrowdFL} can effectively protect participants privacy and
is feasible and efficient.
