This paper explores the potential for communication-efficient federated learning (FL) in
modern distributed systems. FL is an emerging distributed machine learning technique that …

Federated learning (FL) is a technique that allows multiple participants to collaboratively
train a Deep Neural Network (DNN) without the need to centralize their data. Among other …

Distributed training across several quantum computers could significantly improve the
training time and if we could share the learned model, not the data, it could potentially …

Deep learning models can enable accurate and efficient disease diagnosis, but have thus
far been hampered by the data scarcity present in the medical world. Automated diagnosis …

Motivation Limited data access has hindered the field of precision medicine from exploring
its full potential, eg concerning machine learning and privacy and data protection rules. Our …

Federated learning (FL) is known to perform machine learning tasks in a distributed manner.
Over the years, this has become an emerging technology, especially with various data …

The integration of the Internet of Things (IoT) with machine learning (ML) is revolutionizing
how services and applications impact our daily lives. In traditional ML methods, data are …

Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL)
to several domains ranging from edge computing to banking. The technique's inherent …

At present, some studies have combined federated learning with blockchain, so that
participants can conduct federated learning tasks under decentralized conditions, sharing …

Federated Learning (FL) is a privacy-preserving way to utilize the sensitive data generated
by smart sensors of user devices, where a central parameter server (PS) coordinates …