Mobile crowdsensing (MCS) has gained significant attention in recent years and has
become an appealing paradigm for urban sensing. For data collection, MCS systems rely on …

With the surging of smartphone sensing, wireless networking, and mobile social networking
techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising …

Federated learning is promising in enabling large-scale machine learning by massive
clients without exposing their raw data. It can not only enable the clients to preserve the …

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 …

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 …

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 …

Crowdsensing applications utilize the pervasive smartphone users to collect large-scale
sensing data efficiently. The quality of sensing data depends on the participation of highly …

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 …

We design and implement the first private and anonymous decentralized crowdsourcing
system ZebraLancer, and overcome two fundamental challenges of decentralizing …

Public blockchain networks using proof of work (PoW)-based consensus protocols are
considered as a promising platform for decentralized resource management with financial …