With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …

The widespread use of mobile devices such as smartphones, tablets, and smartwatches has
led users to constantly generate various location data during their daily activities …

Federated learning (FL), as a type of distributed machine learning, is capable of significantly
preserving clients' private data from being exposed to adversaries. Nevertheless, private …

Federated learning (FL), as a type of collaborative machine learning framework, is capable
of preserving private data from mobile terminals (MTs) while training the data into useful …

In federated learning (FL), model training is distributed over clients and local models are
aggregated by a central server. The performance of uploaded models in such situations can …

Federated learning (FL) can protect clients' privacy from leakage in distributed machine
learning. Applying federated learning to edge computing can protect the privacy of edge …

Collecting and analyzing massive data generated from smart devices have become
increasingly pervasive in crowdsensing, which are the building blocks for data-driven …

Differential privacy has been a de facto privacy standard in defining privacy and handling
privacy preservation. It has had great success in scenarios of local data privacy and …

With the advent of the era of big data, privacy issues have been becoming a hot topic in
public. Local differential privacy (LDP) is a state‐of‐the‐art privacy preservation technique …

This article investigates the secure and privacy-preserving consensus problem of multiagent
systems (MASs) with directed interaction topologies under multiple cyberattacks, which …