A Taxonomy to Understand Federated Learning
A Taxonomy to Understand Federated Learning
Classifying different types of federated learning methods, Meta AI research about highly scalable and asynchronous federated learning pipelines and Microsoft's FLUTE framework.
In this issue:
A taxonomy for classifying federated learning methods.
Meta AI research for building highly scalable and asynchronous federated learning pipelines
Microsoft Research’s FLUTE framework for federated learning architectures.
💡 ML Concept of the Day: A Taxonomy to Understand Federated Learning
One of the most common mistakes around federated learning, is to think about it as a single type of architecture. Even though while the principles of federated learning are unique enough to merit its own space within deep learning, there are different architectures that implement those principle in diverse way. While there is no consistent taxonomy to study federated learning, there are some categorization schemes that are proven to be quite useful. The following taxonomy might be relevant to understand the different variations of federated learning architectures: