As enterprises struggle to balance AI capabilities against data privacy concerns, federated learning provides the best of both worlds.

Federated Learning solves two big problems of data analysis: improved qualitative analyses for society and safeguarding of one's privacy.

In recent decades, computer scientists have been developing increasingly advanced machine learning techniques that can learn to predict specific patterns or effectively complete tasks by analyzing ...

Federated learning’s popularity is rapidly increasing because it addresses common development-related security concerns.

Federated Learning (FL) stands at the intersection of privacy preservation and decentralized data use, revolutionizing practical machine learning. This approach maintains data on local devices ...

Federated learning is essentially machine learning for inaccessible data—the data could be private, or the data owner may not want to lose ownership.

These revolutionary technologies are poised to redefine our interaction with AI, and their integration across varied sectors is only set to accelerate.

DynamoFL, a startup developing novel federated learning techniques, has raised a seed round of funding to fuel its quest to bring privacy-preserving AI training techniques to more industries.

Federated learning lets a network of participants collaboratively train algorithms on data while keeping each stakeholder's data in its home location.