With the introduction of Google's Tensor Flow federated, the hype around federated machine learning is surging. But there are important questions about data privacy, performance and cost that need ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

One of the key challenges of machine learning is the need for large amounts of data. Gathering training datasets for machine learning models poses privacy, security, and processing risks that ...

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 reliance on public data — mostly web data — to train AI is holding back the AI field. That’s according to Daniel Beutel, a tech entrepreneur and researcher at the University of Cambridge, who ...

Federated learning makes it possible for agency employees to collaborate on advanced artificial intelligence models without compromising data control or operational security. The process serves as a ...

Let’s imagine a fictional company, Global Retail Corporation, a multinational retail chain struggling with its initial approach to AI integration. They built custom generative AI applications on their ...

Expertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. Artificial intelligence (AI) is poised to revolutionize the medical field, offering ...

What if we could learn from massive collections of data while avoiding the privacy and other risks typically associated with sharing such information? The Mayo Clinic has taken a step toward making ...