Discover how Quantum AI protects your data—even against future-proof hackers. Learn how Quantum Federated Learning (QFL) combines speed, privacy, and resilience.| Blue Headline
GPU-powered machine learning achieves 159x faster intrusion detection for the Internet of Vehicles (IoV)—without compromising accuracy. Discover how.| Blue Headline
In Part 2 of our Federated Learning series, learn how to submit a machine learning job to real, remote datasites using the SyftBox network—without accessing private data. A practical guide for Data Scientists moving from local simulation to distributed networks.| OpenMined
Learn how hospitals used federated learning to collaboratively train a diabetes prediction model without sharing sensitive patient data. Includes code walkthrough.| OpenMined
OpenFL-XAI has been designed and implemented by the Artificial Intelligence R&D Group (UPI AI Group) at the Department of Information Engineering, University of Pisa, as part of the technical activities on “Connecting intelligence for 6G networks” carried out jointly by University of Pisa, TIM and Intel Corporation in the framework of the 6G Flagship EU project Hexa-X. The post OpenFL-XAI: the open-source tool for Federated Learning of explainable AI models first appeared on Hexa-X.| Hexa-X