Marie Roehm

Collaborative data ecosystems in pharma securely connect life sciences and healthcare data to drive AI-powered insights or advanced analytics on these distributed datasets. They enable shared use of real-world data without exposing sensitive information, overcoming data silos while ensuring privacy and compliance.

Companies are benefiting from federated learning by being able to access data without privacy and IP risks associated with copying or centralizing data.

More businesses are waking up to the value of data and the necessity of data collaboration in harnessing its power.

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.

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.

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.

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.

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.

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.

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.

How can you evaluate platforms around emerging technologies like federated learning? This article gives you guidance on what your selection criteria should be.