Abstract Federated-Learning (FL), a new paradigm in the machine-learning approach,
wherein the clients train the global model collaboratively across various computational …

The rapid development of the Internet and smart devices trigger surge in network traffic
making its infrastructure more complex and heterogeneous. The predominated usage of …

With the growing concern on data privacy and security, it is undesirable to collect data from
all users to perform machine learning tasks. Federated learning, a decentralized learning …

Federated learning (FL) is a secure distributed machine learning paradigm that addresses
the issue of data silos in building a joint model. Its unique distributed training mode and the …

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis
on enabling software and hardware platforms, protocols, real-life applications and use …

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

A need to enhance healthcare sector amidst pandemic arises. Many technological
developments in Artificial Intelligence (AI) are being constantly leveraged in different fields of …

Federated learning is a set-up in which multiple clients collaborate to solve machine
learning problems, which is under the coordination of a central aggregator. This setting also …

Federated learning is a new type of distributed learning framework that allows multiple
participants to share training results without revealing their data privacy. As data privacy …

Federated Learning (FL) breaks the “data island” and lets clients cooperate in training a
shared model with private data locally. And hierarchical framework is used in FL to alleviate …