Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

Federated Learning (FL) is a distributed machine learning approach that allows multiple
parties to collaboratively train a model without sharing raw data, thus protecting data privacy …

Federated Continual Learning (FCL) has emerged as a promising paradigm that combines
Federated Learning (FL) and Continual Learning (CL). To achieve good model accuracy …

This study introduces novel deep learning (DL) techniques for effective fitness prediction
using a person's health data. Initially, pre-processing is performed in which data cleaning …

Mobile apps, such as mHealth and wellness applications, can benefit from deep learning
(DL) models trained with mobile sensing data collected by smart phones or wearable …

Federated Learning (FL) has emerged as a new distributed Deep Learning (DL) paradigm
that enables privacy-aware training and inference on mobile devices with help from the …

The strict latency constraints of emerging vehicular applications make it unfeasible to
forward sensing data from vehicles to the cloud for processing. Fog computing shortens the …