Model Aggregator App

This app, together with the pretrained_model_aggregator app, form an aggregation pipeline for pretrained MNIST models using SyftBox

Running in dev mode

0. Reset everything if necessary with just reset. Clone the pretrained_model_local and pretrained_model_aggregator branches into the current directory, e.g. with the command git clone --branch pretrained_model_local https://github.com/OpenMined/tutorial-apps.git pretrained_model_local
1. Run upto 11 clients (a, b, c...) with just run-client <client_name> and the SyftBox cache server just run-server. Here, a will be the model aggregator
2. Install the local pretrained app on b, c, ...: syftbox app install pretrained_model_local --config_path <path_to_config.json> where <path_to_config.json> points to b or c or other clients' config.json file
3. Install the model aggregator app on a: syftbox app install pretrained_model_aggregator --config_path <path_to_config.json> where <path_to_config.json> points to a's config.json file
4. Move the MNIST pretrained model in b, c, or other clients' private folder into public to let it be aggregated by a
5. a will automatically look for and aggregate the models from other clients. Check a's SyftBox client log to see if the global model accuracy has improved, look for key words "Aggregated models from" and "Global model accuracy"

Running live as a model submitter

0. Clone the pretrained_model_local branch into the current directory, e.g. with the command git clone --branch pretrained_model_local https://github.com/OpenMined/tutorial-apps.git pretrained_model_local
1. Run your syft client with syftbox client
2. Install the app with syftbox app install pretrained_model_local
3. Go to your sync folder's datasite, you will see that you have a pretrained MNIST model in your private/ folder, e.g. pretrained_mnist_label_2.pt
4. Submit your model for aggregation by copying / dragging the pretrained model from the private/ folder into the public/ folder. Note that the model aggregator needs to list you as a participant for your model to be aggregated