Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Federated Learning (FL) has emerged as a promising approach to address privacy
concerns inherent in Machine Learning (ML) practices. However, conventional FL methods …

Mobile devices have become an essential element in our day-to-day lives. The chances of
mobile attacks are rapidly increasing with the growing use of mobile devices. Exploiting …

Abstract Federated Learning (FL, or Collaborative Learning (CL)) has surely gained a
reputation for not only building Machine Learning (ML) models that rely on distributed …

This article considers reliable and secure spectrum sensing (SS) based on federated
learning (FL) in the cognitive radio (CR) environment. Motivation, architectures, and …

Federated Learning (FL) has emerged as a transformative paradigm in machine learning,
enabling decentralized model training across multiple devices while preserving data …

In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big
Data, intricately designed to ensure privacy preservation across extensive system networks …

Federated learning (FL) is the most popular distributed machine learning technique. FL
allows machine-learning models to be trained without acquiring raw data to a single point for …

Due to the increase in data regulations amid rising privacy concerns, the machine learning
(ML) community has proposed a novel distributed training paradigm called federated …