Federated learning (FL) is an approach within the realm of machine learning (ML) that
allows the use of distributed data without compromising personal privacy. In FL, it becomes …

As a new distributed machine learning framework, Federated Learning (FL) effectively
solves the problems of data silo and privacy protection in the field of artificial intelligence …

Federated Learning (FL) trains machine learning models on data distributed across multiple
devices, avoiding data transfer to a central location. This improves privacy, reduces …

Abstract Are Federated Learning (FL) systems free from backdoor poisoning with the arsenal
of various defense strategies deployed? This is an intriguing problem with significant …

Federated edge learning can be essential in supporting privacy-preserving, artificial
intelligence (AI)-enabled activities in digital twin 6G-enabled Internet of Things (IoT) …

Federated learning (FL) enables decentralized training of deep neural networks (DNNs) for
object detection over a distributed population of clients. It allows edge clients to keep their …

Federated learning enables multi-participant joint modeling with distributed and localized
training, thus effectively overcoming the problems of data island and privacy protection …

Federated learning (FL) is a privacy-preserving machine learning paradigm that enables
multiple clients to train a unified model without disclosing their private data. However …

Recently, federated learning has received widespread attention, which will promote the
implementation of artificial intelligence technology in various fields. Privacy-preserving …

The potential of Federated Learning (FL) deployment increases rapidly as the number of
connected devices increases, the value of artificial intelligence is recognized and …