This non-centralized approach offers distinct data privacy advantages, making it especially suitable for applications with strict privacy policies, such as consumer-centric healthcare systems. However ...

Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm via multiple independent sessions, each using its own dataset.

Abstract: Bayesian Federated Learning (FL) policies enable multiple nodes to collaboratively train a shared ... yet well-calibrated ML models. The analysis is carried out in the healthcare domain, ...

The convergence of blockchain, federated learning, and cloud AI addresses critical data privacy and security challenges.

Flower (flwr) is a framework for building federated AI systems. The design of Flower is based on a few guiding principles: Customizable: Federated learning systems vary wildly from one use case to ...

Federated learning is a classic of privacy-preserving learning, which enables collaborative learning without sharing data. Structured data has become the mainstream of current applications, where ...

Researchers at the Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS) have played a key role in the EU-funded INCISIVE project, which has enabled the creation of AI-based ...