Our »Distributed Analytics Runtime for Federated Learning« (Fed-DART) enables the easy implementation of federated machine learning (ML) methods to leverage local data of distributed environments. This makes it possible to train an AI model without having to centralize and merge the data.
Increasing amounts of data are needed to train AI. The more data available for training, the better the results. In the practical implementation of many AI projects, obtaining or providing this data is a significant hurdle. The reasons for this can be manifold:
- Individual companies or departments can only acquire a small amount of data or cannot bring it together centrally.
- Regulatory restrictions due to data sovereignty and data protection
- Data is generated on mobile devices and cannot be combined into a large database due to low communication bandwidth. Training on all data at the same time is thus not possible.