AI Training
All customers receive an individual training based on the global model and firm specifications.
Each single model is trained on the individual customer premise. No data needs to be uploaded onto our platform.
FEDERATED LEARNING AS A SERVICE
Because privacy matters
Protecting the privacy of personal and sensitive data is the most delicate topic for many AI engineers. XAIN provides a Federated Learning platform, so your machine learning pipeline becomes automatically compliant in privacy regulations. No more anonymization needed, no more risks of data consolidation.
FEDERATED LEARNING
The privacy preserving way
Our platform leverages Federated Learning, the innovative approach of training data decentralized and efficient. This makes machine learning compliant to privacy regulations, such as EU-GDPR and CCPA. Find out how this technology works and why it makes complicated and costly anonymization obsolete.
Discover also the following resources
Case Study
The first AI application powered by XAINs FedML technology
ANDY is the first application running its training models on the eXpandable AI Network.
The solution for automated invoice processing consolidates machine learning knowledge from various data silos while keeping data privately in the respective corporate environments.
How it works?
Model Optimization
The training outcome is an initial learning based on the customer’s own data.
This knowledge is then being consolidated in a global training model where the model is optimized through a weighted average. No link can be drawn to the data used.
Distribution
The model optimum is then distributed to the customers' applications to update and improve each individual model based on the collective knowledge acquired.
Thus, companies are able to train well performing machine learning models - even with less training data available!
We are proud to have already successfully worked with:
Get in touch
Interested in more information about XAIN, insights about Federated Machine Learning or you want to work with us?