  Crowdsensing is a promising sensing paradigm for smart city applications
(e.g., traffic and environment monitoring) with the prevalence of smart mobile
devices and advanced network infrastructure. Meanwhile, as tasks are performed
by individuals, privacy protection is one of the key issues in crowdsensing
systems. Traditionally, to alleviate users' privacy concerns, noises are added
to participants' sensitive data (e.g., participants' locations) through
techniques such as differential privacy. However, this inevitably results in
quality loss to the crowdsensing task. Recently, federated learning paradigm
has been proposed, which aims to achieve privacy preservation in machine
learning while ensuring that the learning quality suffers little or no loss.
Inspired by the federated learning paradigm, this article studies how federated
learning may benefit crowdsensing applications. In particular, we first propose
a federated crowdsensing framework, which analyzes the privacy concerns of each
crowdsensing stage (i.e., task creation, task assignment, task execution, and
data aggregation) and discuss how federated learning techniques may take
effect. Finally, we summarize key challenges and opportunities in federated
crowdsensing.
