Training an image classifier with CIFAR10 dataset

We will use the original Training a Classifier example from pytorch as the code base.

The CIFAR10 dataset has the following classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’, ‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size.

In the following examples, we will show various Federated Learning workflows and features:

stats - Federated statistics image intensity histogram calculation.
sag - FedAvg with Client API
sag_deploy_map - FedAvg with site-specific configs.
sag_executor - FedAvg with Executor API
sag_mlflow - FedAvg with MLflow experiment tracking logs.
sag_he - FedAvg with homomorphic encryption using POC -he mode.
cse - Cross-site evaluation with server-side controller.
cyclic - Cyclic Weight Transfer (cyclic) workflow with server-side controller.
cyclic_ccwf - Client-controlled cyclic workflow with client-side controller.
swarm - Swarm learning and client-controlled cross-site evaluation.