BIOENGINEER

Federated learning framework aims to stabilize colorectal cancer CT classification across hospitals

A new personalized federated learning framework uses Transformer-based dynamic aggregation and adaptive regularization to ...
BIOENGINEER

Federated, explainable AI framework classifies Alzheimer’s disease from MRI and clinical data

A federated learning framework combining MRI and clinical data classifies Alzheimer's disease with reported accuracy up to 94 ...

Researchers develop privacy-friendly, secure and sustainable AI techniques for medical imaging

Artificial intelligence can be helpful in analyzing medical images and support radiotherapy planning workflows. However, huge ...
Mirage News

Federated Learning Enables Joint AI Development Across Institutions

Medical imaging supports the planning of modern radiation therapy, with federated AI approaches possibly opening up new ...
techtarget

Federated Learning Framework Aims to Improve Fairness in AI Screening Tools

A research team from the University of Pittsburgh (Pitt) Swanson School of Engineering has been awarded a $1.7 million National Institutes of Health grant to develop a federated learning (FL)-based ...
EurekAlert!

DFUN-KDF: A knowledge distillation-based decentralized federated learning framework for uav network optimization

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, ...
Devdiscourse

AI Framework Boosts 6G Traffic Forecasts and Threat Detection in Benchmark Tests

FALCON-6G combines federated learning and language models to improve network traffic prediction and intrusion detection. Benchmark tests showed better accuracy, faster decisions and lower ...
Forbes

IBM Federated Learning Research - Extracting Machine Learning Models From Multiple Data Pools

As machine learning becomes more pervasive in the data center and the cloud there will be a need to share and aggregate information and knowledge but without exposing or moving the underlying data.