Federated Learning with Weights & Biases

In this workspace we can see an example of a federated learning project that utilised Weights & Biases (W&B). This project used the Python federated learning framework Flower to train 3 clients on subsets of the CIFAR-10 image classification dataset.

Federated Learning enables learning from large distributed datasets in a privacy preserving manner where it is not possible due to technical or legal reasons to centralise the data for non-federated machine/deep learning.