In this issue:

1. An overview of federated transfer learning.

2. The TorchFL reference architecture for federated learning pipelines.

3. The OpenFL framework.

💡 ML Concept of the Day: Federated Transfer Learning

In the last two editions of this series, we explored the concepts of horizontal (HFL) and vertical (VFL) federated learning. These two architectures are applicable when nodes in a federation have a high overlap on either the feature or sample space. However, there are many scenarios in which these overlaps are relatively small but still viable for a federated learning scenario. This is where federated transfer learning (FTL) comes into play as a variation of the HFL and VFL architectures that is very applicable in such scenarios...