What we're about

Let’s discuss Federated Learning!

This group has been created to provide the machine learning (ML) community a place to exchange information and experiences around the innovative ML approach of Federated Learning, also known as Federated Machine Learning.

Whether you are an AI engineer, developer, data scientist, privacy officer, lawyer or simply a generally interested person, this group is for you! We welcome everybody to join that is interested in tech deep dives, use cases, and their applications.

This group is specifically meant to be interactive and collaborative. We welcome everybody to share ideas and knowledge in the field. If you would like to propose meetup topics, either to present yourself or because you’d like to learn more about, we’d love to hear from you under meetup@xain.io. You can also use this email address for questions and concerns.

Please also take a minute to read our Code of conduct: https://www.xain.io/coc

If you are new to the topic, feel free to dive deeper into the following resources:

• An introduction to Federated Learning: https://www.xain.io/federated-machine-learning

• Video ‘Federated Learning explained in 90 seconds’:

• An introduction to the privacy aspects of AI and ML: https://medium.com/xain/an-introduction-to-xains-gdpr-compliance-layer-for-machine-learning-f7c321b31b06

• Find our Apache Open Source end-to-end Federated Learning stack here: https://github.com/xainag

Our tech stack:

• Python 3.6

• TensorFlow 1.14.0 migrating to 2.0.0

• NumProto v0.3.0

Good to know
In 2020, we will start hosting a series of meetups.

About XAIN

At XAIN, we build solutions that bring enterprises to the forefront of AI utilization. We tackle the dilemma of unlocking the full power of AI without compromising data privacy. XAIN’s origin was a joint research project by CEO Leif-Nissen Lundbaek and  Imperial College London Professor Michael Huth  as well as CTO at XAIN.

Research is in our DNA and today serves as our driving force for further innovation. XAIN aims to solve some of the biggest challenges in the research field of privacy-preserving AI, particularly focusing on Federated Machine Learning.

Past events (1)

Introduction to Federated Learning

Xayn AG

Photos (7)

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