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Apheris achieves top information security certification, ISO 27001

Certification highlights our mission and focus on best practices for federated machine learning and analytics, enabling organizations to securely build and operationalize machine learning and data applications across boundaries.

Apheris raises €8.7m to power development of smarter AI and collaboratively solve the world’s biggest challenges

Seed extension round led by Octopus Ventures to drive growth of platform enabling organizations to unlock terabytes of valuable data risk-free

Apheris launches platform to unlock data and enable collaboration to solve the world’s biggest problems

The Apheris Platform enables multiple organisations to extract value from each other’s decentralised data sets and overcome regulatory, technical, and commercial challenges.

Large Language Model: in Conversation with ChatGPT

In conversation with ChatGPT about the relationship between large language models (LLMs) and federated learning.

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.

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

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.

Data without borders

Organizations across the globe are quickly waking up to the need for safe, hassle-free data collaboration without the limitations, barriers, and security issues associated with data sharing. The Apheris platform for federated machine learning and analytics is making secure data collaboration across boundaries a reality.

Challenges with implementing federated learning

Explore some of the common challenges organizations face when implementing federated learning and key considerations that can help them overcome those challenges and reap the rewards of working with federated data.

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.

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.

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.

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.

The Collaborative Potential of Federated Machine Learning

There are incredible opportunities to be seized through federated machine learning across many industries. While some early innovators have recognized the potential, many have yet to seize the opportunity before them.

Unlock unprecedented collaboration potential

Following its 77th session, the UN General Assembly championed collaboration as the cement that could strengthen individuals, organizations, and industries against both current and future challenges. Read on to find out how.

Data collaboration vs data sharing

It's often assumed that data sharing is required in order to derive value from data held across multiple organizations in a value chain. However, data sharing comes with challenges. Working with federated data, however, addresses these challenges and allows for better collaboration across organizational boundaries.

The Secret to Collaborative Data Ecosystem Success

Data is widely considered to be the lifeblood of modern organizations, but it’s only as valuable as the insights that can be mined from it. A collaborative data ecosystem helps organizations come together to mine much-needed insights.

The State of Data Collaboration – why we are asking for your help

Participate in our survey and shape the future of Data Collaboration. For a completed survey you will recieve a report that will help to benchmark yourself and your company on your collaborative ecosystem efforts.

The big data opportunities: manufacturing

In order to rise above today’s challenges, manufacturers need to come together, pool their collective assets and insights, and create modern solutions to modern problems.

A time to reflect and look forward

As we near the end of the year we take a look at key themes from 2022 and what to expect in 2023. Here are our top predictions for the year ahead.

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.

The Future of Data Products – 6 Industry Trends For Frontrunners

If you’ve been successful to date with building industry-leading data products, you don’t want to rest on your laurels. Instead, you should be trying to extend your lead. However, this can be daunting task in an industry that is in constant flux as the data, machine learning and AI landscape.

The Five Qualities of Collaborative Data Ecosystems

Over the past decade, organizations have focused heavily on implementing the culture, tools, and processes to create value from data with data science and machine learning within the enterprise. But this transformation doesn't intend to stop at corporate boundaries. Read the article to find out more

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