Governed, secure, and private computational access to federated patient data is revolutionizing healthcare AI
MedTech AI revolution depends on computational access to fine-grained patient data to train enhanced ML models. Apheris provides governed, secure, and private computational access to data for ML, enabling fast and easy FDA approval for new MedTech data products.
Celebrating women's contribution to STEM fields
11 February is the International Day of Women and Girls in Science. We interview Evelyn, Data Science Experience Lead at Apheris, and give you an insight into the life of a successful woman in science.
7 Myths About Federated Learning
In our daily work as a company that builds a platform for federated and privacy-preserving data science, we are often asked to clarify concepts around federated learning with customers. This article highlights 7 common myths about federated learning (FL) and, using practical examples, shows you exactly why they are misleading.
The Three Adoption Stages of Privacy-enhancing Technologies (And Why We Are Stuck on Level Two)
PETs are massively changing how we operate, and how we have to think about the data and AI landscape. Introducing such a game-changer into large enterprises has to be done with the highest precision, and a lot of foresight.
7 Value Drivers of Complementary Data - Your Path to Unlocking the Benefits of Federated Data
There is only value in data if it can be used, and if there is appropriate access to that data when it is needed. Learn how you can turn decentralized, federated data into a complementary strategic data asset.
How data collaboration is changing the world
More businesses are waking up to the value of data and the necessity of data collaboration in harnessing its power.
Refine large language models to boost AI performance
Open AI's ChatGPT is very popular, yet not ready for enterprise adoption due to accuracy, security & privacy concerns. By refining large language models this can be tackled. For organizations that operate in a regulated space, federated learning can be used to train on distributed data to unlock value while maintaining privacy.
Securing ML Models: Apheris' Contribution to ML Security
Together with the German Federal Office for Information Security we've developed frameworks and recommendations for ML practitioners to help secure ML models and maintain appropriate security measures.
Top 7 Open-Source Frameworks for Federated Learning
Open-source frameworks for federated learning are a great way of getting first hands-on experience. Here are our Top 7 with their respective pro and cons
How to Choose the Best Federated Learning Platform
How can you evaluate platforms around emerging technologies like federated learning? This article gives you guidance on what your selection criteria should be.