Abstract Federated Learning (FL) and Industry 5.0's convergence holds significant promise
for changing smart systems. FL, a distributed machine learning method, allows for …

When data privacy is imposed as a necessity, Federated learning (FL) emerges as a
relevant artificial intelligence field for developing machine learning (ML) models in a …

Federated learning (FL) is a machine learning system that allows a network of devices to
train a model without centralized data. This characteristic makes FL an ideal choice for …

The convergence of the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) has
given rise to Industry 4.0, creating a wealth of opportunities for manufacturing companies …

Abstract While Federated Learning (FL) provides a privacy-preserving approach to analyze
sensitive data without centralizing training data, the field lacks an detailed comparison of …

In this position paper, we underscore the critical need for a systematic and structured
approach to comparing Federated Learning (FL) frameworks. Given the diversity of FL …

Federated learning (FL) brings collaborative intelligence into industries without centralized
training data to accelerate the process of Industry 4.0 on the edge computing level. FL …

Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

As an emerging technique, Federated Learning (FL) can jointly train a global model with the
data remaining locally, which effectively solves the problem of data privacy protection …

This work investigates fine-grained data distribution in real-world federated learning (FL)
applications, wherein training samples are distributed across multiple regions, and different …