Federated learning (FL) is a distributed deep learning framework that has become
increasingly popular in recent years. Essentially, FL supports numerous participants and the …

A successful machine learning (ML) algorithm often relies on a large amount of high-quality
data to train well-performed models. Supervised learning approaches, such as deep …

Federated learning (FL) is a promising paradigm for collaboratively training networks on
distributed clients while retaining data locally. Recent work has shown that personal data …

In this work, the problem of training the secure federated learning (FL) algorithm over a
multicell wireless network is investigated. FL is indeed a learning method that can protect …

Federated learning (FL) emerged as a paradigm designed to improve data privacy by
enabling data to reside at its source, thus embedding privacy as a core consideration in FL …

In recent years, the need for privacy preservation when manipulating or storing personal
data, including speech, has become a major issue. In this paper, we present a system …

Federated learning (FL) is an emerging distributed machine learning paradigm proposed for
privacy preservation. Unlike traditional centralized learning approaches, FL enables multiple …

Federated Learning (FL) has emerged as a popular paradigm for collaborative learning
among multiple parties. It is considered privacy-friendly because local data remains on …

Federated Learning exploits local model training to aggregate and create a global model
without sharing raw data. Each client trains a local model and shares it to aggregate a global …

Android malware detection has become research hotspot in mobile security. When security
service providers obtain feature information from target samples, they may involve user …