Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …

With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis
on enabling software and hardware platforms, protocols, real-life applications and use …

Federated Learning (FL) bridges the gap between collaborative machine learning and
preserving data privacy. To sustain the long-term operation of an FL ecosystem, it is …

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …

Federated learning (FL) is a privacy-preserving distributed learning paradigm that enables
clients to jointly train a global model. In real-world FL implementations, client data could …

Federated learning (FL) has been widely recognized as a promising approach by enabling
individual end-devices to cooperatively train a global model without exposing their own …

Nowadays, data privacy is an important consideration in machine learning. This paper
provides an overview of how Federated Learning can be used to improve data security and …

Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized
devices with labeled data in a privacy-preserving manner. However, data with noisy labels …

Recently, federated learning (FL) has achieved wide successes for diverse privacy-sensitive
applications without sacrificing the sensitive private information of clients. However, the data …