Privacy & security for federated learning

Assessing readiness for federated machine learning & analytics
In a world where data is pivotal, its full potential is often overshadowed by risks. As foundation models proliferate, data emerges as the unique advantage. The challenge? Leveraging it safely.
Our short ebook introduces Apheris' solution for secure computational access to data, connecting federated data sets for ML without the risks of sharing or centralization. A primer for data readiness for federated learning, prioritizing security and privacy.
Published 9 August 2023

In a world where data stands as the backbone of an organization's success, are we maximizing its value or leaving it exposed and underutilized? Amidst the rise of commoditized model architectures, the spotlight now falls on data as the primary driving force for differentiation. Yet, the crucial challenge remains: How can businesses effectively tap into this goldmine without risking its sanctity?

This short ebook introduces Apheris' cutting-edge solution for secure computational access to data, connecting federated data sets for ML and analytics without data sharing or centralization. Dive into the essence of robust data governance and learn actionable steps for data security assessment. Concluding with the "Five Safes" framework, discover the balance between data security, privacy, and collaboration. This guide serves as an introduction to ensuring your data is primed for federated learning, focusing on security and privacy. For technical leaders eager to take the first step, immerse yourself and lay the foundations for governed, private, secure ML.


Federated learning & analytics
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