Federated learning (FL) has nourished a promising method for data silos, which enables
multiple participants to construct a joint model collaboratively without centralizing data. The …

Federated learning (FL) is a distributed Machine Learning (ML) approach that allows
multiple clients to train a global model collaboratively while maintaining their data …

The growth of data generation capabilities, facilitated by advancements in communication
and computation technologies, as well as the rise of the Internet of Things (IoT), results in …

Federated learning (FL), as a distributed machine learning approach, has drawn a great
amount of attention in recent years. FL shows an inherent advantage in privacy preservation …

Federated learning (FL) is a technique that involves multiple participants who update their
local models with private data and aggregate these models using a central server …

Traditional federated learning addresses the data security issues arising from the need to
centralize client datasets on a central server for model training. However, this approach still …

While centralized servers pose a risk of being a single point of failure, decentralized
approaches like blockchain offer a compelling solution by implementing a consensus …

Federated learning (FL) serves as an enabling technology for intelligent edge computing,
where high-quality machine learning (ML) models are collaboratively trained over large …

As a key technology in 6G research, federated learning (FL) enables collaborative learning
among multiple clients while ensuring individual data privacy. However, malicious attackers …

The present study thoroughly evaluates the most common blocking challenges faced by the
federated learning (FL) ecosystem and analyzes existing state-of-the-art solutions. A system …