Federated learning (FL) rests on the notion of training a global model in a decentralized
manner. Under this setting, mobile devices perform computations on their local data before …

Mobile crowdsensing engages a crowd of individuals to use their mobile devices to
cooperatively collect data about social events and phenomena for customers with common …

Mobile crowdsourcing is an emerging crowdsourcing paradigm, which generates large-
scale sensing tasks and sensing data. One of the major issues in mobile crowdsourcing is …

With the development of Mobile Crowd Sensing Networks (MCSN), more and more Mobile
Crowdsourcing applications emerge. The mobile sensing technologies and theories have …

Mobile crowd-sensing is a prospective paradigm especially for intelligent mobile terminals,
which collects ubiquitous data efficiently in metropolis. The existing crowd-sensing schemes …

Nowadays, vehicles have been increasingly adopted in mobile crowdsensing applications.
Due to their predictable mobility trajectories, vehicles as participants bring new insight in …

As a new paradigm to solve problems by gathering the intelligence of crowds, mobile
crowdsourcing has become one of the hot spots in academic and industrial fields. Task …

Federated Learning (FL) is a new distributed machine learning (ML) approach which
enables thousands of mobile devices to collaboratively train artificial intelligence (AI) models …

With the widespread prevalence of smart devices, mobile crowdsensing (MCS) becomes a
new trend to encourage mobile nodes to participate in cooperative data collection in various …

Spatial Crowdsourcing (SC) is a novel platform that engages individuals in the act of
collecting various types of spatial data. This method of data collection can significantly …