Learn about Federated Machine Learning and how it is applied in the industry today.
A lot of high potential ML solutions struggle to be realised for the same reason; It can be impossible, illegal or inappropriate to move the data that the ML model needs to get trained. This is a problem for...
- ML in the area of self driving vehicles (practical data transfer issues)
- ML based on data that is owned by someone else (data ownership issues)
- ML based on user data on mobile devices (eg. data privacy issues)
.. to name a few examples.
Federated Machine Learning resolves these barriers for ML. With Federated Machine Learning, the model training data stays local on the device or with a local data owner and instead of moving the data to the model, you move the model to the data. In this session you will learn about Federated Machine Learning and how it is applied in the industry today.