-
Write something in the document below!
- There is at least one public document in every node in the Agora. Whatever you write in it will be integrated and made available for the next visitor to read and edit.
- Write to the Agora from social media.
-
Sign up as a full Agora user.
- As a full user you will be able to contribute your personal notes and resources directly to this knowledge commons. Some setup required :)
federated learning
Go back to the [[AI Glossary]]
A distributed machine learning approach that trains machine learning models using decentralized examples residing on devices such as smartphones. In federated learning, a subset of devices downloads the current model from a central coordinating server. The devices use the examples stored on the devices to make improvements to the model. The devices then upload the model improvements (but not the training examples) to the coordinating server, where they are aggregated with other updates to yield an improved global model. After the aggregation, the model updates computed by devices are no longer needed, and can be discarded.
Since the training examples are never uploaded, federated learning follows the privacy principles of focused data collection and data minimization.
For more information about federated learning, see this tutorial.
- public document at doc.anagora.org/federated_learning|federated-learning
- video call at meet.jit.si/federated_learning|federated-learning