Distributed analytics facilitate to make the data-driven services smarter for a wider range of
applications in many domains, including agriculture. The key to producing services at such …

Federated learning is a distributed machine learning framework that enables distributed
nodes with computation and storage capabilities to train a global model while keeping …

Abstract The Fourth Industrial Revolution suggests smart and automated industrial solutions
by incorporating Artificial Intelligence into it. Today, the world of technology is highly …

Federated Learning (FL) presents a decentralized approach to model training in the agri-
food sector and offers the potential for improved machine learning performance, while …

Federated learning (FL) is a collaborative, decentralized privacy‐preserving method to
attach the challenges of storing data and data privacy. Artificial intelligence, machine …

Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …

The data generated and stored in mobile devices owned by individuals as well as in various
organizations contains a large amount of valuable and important information that can be …

Federated learning (FL) is a machine learning approach that decentralizes data and its
processing by allowing clients to train intermediate models on their devices with locally …

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 is a distributed, privacy-preserving machine learning model that is
gaining more attention these days. Federated Learning has a vast number of applications in …