We will use the original Training a Classifer example in 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 - Scatter and Gather (SAG) workflow with Client API
- sag_deploy_map - SAG with deploy_map configuration for deployment of apps to different sites.
- sag_model_learner - SAG with Model Learner API
- sag_executor - SAG with Executor API
- sag_mlflow - SAG with MLflow experiment tracking logs.
- sag_he - SAG with homomorphic encyption 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.