The rise of FML allows systems to become progressively more intelligent and autonomous by using on-device data and sharing only encrypted model updates.

The new capabilities are designed to enable enterprises in regulated industries to securely build and refine machine learning ...

Unlock the synergy of blockchain and machine learning! This guide explores building decentralized models, enhancing accessibility and fostering innovation.

For years, cloud computing was synonymous with centralization. That era is over. Enter: the distributed cloud.

Nous's DisTrO, Distributed Training Over-the-Internet, allows foundation class models to be trained without expensive superclusters.

Machine learning models—especially large-scale ones like GPT, BERT, or DALL·E—are trained using enormous volumes of data.

In this article we explore how privacy-preserving distributed machine learning from federated databases might assist governance in health care. The article first outlines the basic parameters of the ...

TensorFlow 0.8 adds distributed computing support to speed up the learning process for Google's machine learning system.