Federated learning is a privacy-preserving machine learning technique that trains models
across multiple devices holding local data samples without exchanging them. There are …

Federated learning (FL) has experienced a boom in recent years, which is jointly promoted
by the prosperity of machine learning and Artificial Intelligence along with emerging privacy …

Federated learning (FL) is a recent development in artificial intelligence, which is typically
based on the concept of decentralized data. As cyberattacks are frequently happening in the …

In this article, we present a comprehensive study with an experimental analysis of federated
deep learning approaches for cyber security in the Internet of Things (IoT) applications …

Federated learning (FL) is a promising framework for distributed machine learning that trains
models without sharing local data while protecting privacy. FL exploits the concept of …

Blockchain technology has emerged as a promising solution to enhance supply chain
transparency and sustainability in the construction industry. However, the widespread …

In privacy-preserving cross-device federated learning, users train a global model on their
local data and submit encrypted local models, while an untrusted central server aggregates …

The growing reliance of industry 4.0/5.0 on emergent technologies has dramatically
increased the scope of cyber threats and data privacy issues. Recently, federated learning …

Due to the rising awareness of privacy and security in machine learning applications,
federated learning (FL) has received widespread attention and applied to several areas, eg …

Blockchain-based federated learning (BCFL) has recently gained tremendous attention
because of its advantages, such as decentralization and privacy protection of raw data …