In recent years, mobile devices are equipped with increasingly advanced sensing and
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …

This article examines the impact of new AI‐related technologies in data mining and big data
on important research questions in crime analytics. Because the field is so broad, the review …

Federated Learning (FL) has been proposed as an appealing approach to handle data
privacy issue of mobile devices compared to conventional machine learning at the remote …

Unsupervised representation learning has achieved outstanding performances using
centralized data available on the Internet. However, the increasing awareness of privacy …

Self-supervised learning (SSL) is capable of learning remarkable representations from
centrally available data. Recent works further implement federated learning with SSL to …

In recent years, the enhanced sensing and computation capabilities of Internet-of-Things
(IoT) devices have opened the doors to several mobile crowdsensing applications. In mobile …

Federated Learning is a novel framework that allows multiple devices or institutions to train a
machine learning model collaboratively while preserving their data private. This …

Federated learning is a privacy-preserving machine learning technique that learns a shared
model across decentralized clients. It can alleviate privacy concerns of personal re …

This work addresses the problem of anonymizing the identity of faces in a dataset of images,
such that the privacy of those depicted is not violated, while at the same time the dataset is …

Federated learning (FL) emerges as a decentralized learning framework which trains
models from multiple distributed clients without sharing their data to preserve privacy …