In recent times, the Internet of Things (IoT) applications, including smart transportation, smart
healthcare, smart grid, smart city, etc. generate a large volume of real-time data for decision …

In this constantly evolving landscape of urbanization, the relationship between technology
and automation, in regards to sustainability, holds immense significance. The intricate …

Abstract Personalized Federated Learning (pFL) not only can capture the common priors
from broad range of distributed data, but also support customized models for heterogeneous …

The booming development of deep learning applications and services heavily relies on
large deep learning models and massive data in the cloud. However, cloud-based deep …

Edge computing is a paradigm that brings computation and data storage closer to the source
of data generation (Hamdan et al., 2020). Unlike traditional cloud computing, where data is …

The trend of adopting Internet of Things (IoT) in healthcare, smart cities, Industry 4.0, and so
on is increasing by means of cloud computing, which provides on-demand storage and …

The Right to be Forgotten gives a data owner the right to revoke their data from an entity
storing it. In the context of federated learning, the Right to be Forgotten requires that, in …

The increasing concern for privacy and the use of machine learning on personal data has
led researchers to introduce new approaches to machine learning. Federated learning is …

Most of the existing FL systems focus on a data-parallel architecture where training data are
partitioned by samples among several parties. In some real-life applications, however …

Federated Learning (FL) facilitates collaborative training of a shared global model without
exposing clients' private data. In practical FL systems, clients (eg, edge servers …