What is Federated Learning?
Federated learning is a decentralized AI model. In contrast to conventional AI methods, Federated Learning brings the models to the data source/client device for training and inferencing, thereby eliminating costs associated with sharing data with the server and network latencies. By running locally, the model is not only personalised for a particular user but preserves privacy by default.
Companies are slowly gravitating to a Federated Learning approach, where the Machine Learning model is trained on the data at its source and the output is then moved for further analysis. By adopting this approach, the user’s data remains inherently private, albeit useful. Instead of obtaining the actual user data, companies can get the output of the Machine Learning model trained at the source.
Read more about it here.
Ichnite™ makes sharing knowledge easier for faster and cheaper results
- Encryption guarantees security
- Reward owners of the data that provides the most information
- Inspired by customer needs
Example silos
- Proprietary databases
- Mobile phones
- People / genetic profiles
- Small / large models
Alchemite™ Analytics can be deployed in cloud, on site, or in combination, depending on customer needs.
Managed deployments are quick, secure and cost effective.
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