Federated Learning (FL) has gained significant attention as a novel distributed machine learning paradigm that enables collaborative model training while preserving data privacy. However, traditional ...

In an era where data breaches make headlines weekly and privacy regulations tighten globally, artificial intelligence faces a fundamental challenge: how to learn from data without compromising privacy ...

By bringing the training of ML models to users, organizations can advance their AI ambitions while maintaining data security.

The study, titled “The Decentralized AI Ecosystem in Healthcare: A Systematic Review of Technologies, Governance, and ...

As the capacity of artificial intelligence (AI) increases at an exponential rate, so do concerns about the privacy of user data. Increasingly, organizations around the world are adopting something ...

Each year, cyberattacks become more frequent and data breaches become more expensive. Whether companies seek to protect their AI system during development or use their algorithm to improve their ...

The project sits at the intersection of privacy-preserving machine learning, distributed systems, and trustworthy AI, with implications for regulatory compliance and real-world deployment of federated ...

In research published in Nature Medicine today, AI biotech company Owkin has demonstrated for the first time that federated learning (FL) can be used to train deep learning models on data from ...