After two and a half years of work, the MLEDGE project (Cloud and Edge Machine Learning), led by Professor Nikolaos Laoutaris at IMDEA Networks, ...

"Traditional AI architectures were built on the assumption that data could be freely centralized. That assumption no longer ...

Anti-forgetting representation learning method reduces the weight aggregation interference on model memory and augments the representation performance.

Abstract: As an ambitious training paradigm, federated learning has garnered increasing attention in recent years, which enables collaborative training of a global model without accessing users’ ...

Learn about DenseNet, one of the most powerful deep learning architectures, in this beginner-friendly tutorial. Understand its structure, advantages, and how it’s used in real-world AI applications.

Vertical federated learning (VFL) allows parties to build robust shared machine learning models based on learning from distributed features of the same samples, without exposing their own data.

Multiple sclerosis (MS) is a chronic neuroinflammatory disease driven by immune-mediated central nervous system damage, often leading to progressive disability. Accurate segmentation of MS lesions on ...

If you’re learning machine learning with Python, chances are you’ll come across Scikit-learn. Often described as “Machine Learning in Python,” Scikit-learn is one of the most widely used open-source ...

ABSTRACT: As cloud computing continues to evolve, managing CPU resources effectively has become a critical task for ensuring system performance and efficiency. Traditional CPU resource management ...

As the use of Unmanned Aerial Vehicles (UAVs) expands across various fields, there is growing interest in leveraging Federated Learning (FL) to enhance the efficiency of UAV networks. However, ...