Federated learning (FL) becomes popular and has shown great potentials in training large-
scale machine learning (ML) models without exposing the owners' raw data. In FL, the data …

Recent years have witnessed the fast proliferation of mobile devices (eg, smartphones and
wearable devices) in people's lives. In addition, these devices possess powerful …

Internet of Things (IoT) generates large amounts of data at the network edge. Machine
learning models are often built on these data, to enable the detection, classification, and …

Crowdsourcing systems which utilize the human intelligence to solve complex tasks have
gained considerable interest and adoption in recent years. However, the majority of existing …

Crowdsourcing is a computing paradigm where humans are actively involved in a
computing task, especially for tasks that are intrinsically easier for humans than for …

Smartphones are programmable and equipped with a set of cheap but powerful embedded
sensors, such as accelerometer, digital compass, gyroscope, GPS, microphone, and …

Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial
and valuable to mobile users, wireless carriers, and city authorities. Predicting cellular traffic …

Recent years have witnessed the emergence of mobile crowd sensing (MCS) systems,
which leverage the public crowd equipped with various mobile devices for large scale …

In crowdsensing, appropriate rewards are always expected to compensate the participants
for their consumptions of physical resources and involvements of manual efforts. While …

Local administrations and governments aim at leveraging wireless communications and
Internet of Things (IoT) technologies to manage the city infrastructures and enhance the …