This project was created for learning MRI images classifier using the Federated Learning method (McMahan B., et al., 2016).

The project structure consists of two following folders:

• distributed_federated_system - contains the implemented federated system, which can be deployed and run on multiple nodes.
• experimtents - includes federated learning experiments using differently distributed data between clients

In order to run the federated system on multiple different nodes, please, take the following steps:

• Move every single envoy_# and director (aggregator) + workspace folders to separate nodes and install required packages using pip install -r requirements.txt
• For every envoy_# download its corresponding data from here and extract all files to the according envoy's data folder.
• On aggregator node:

  • Set listen_host and listen_port parameters in director.yaml

  • Open director folder in cmd or bash and run the following command to start the director:

    fx director start --disable-tls -c director_config.yaml

• On envoys nodes:

  • Open envoy_# folder in cmd or bash and run the following command to start the envoy:

    fx envoy start -n ${ENVOY_NAME} --disable-tls --envoy-config-path envoy_config.yaml -dh ${AGGREGATOR_FQDN} -dp ${AGGREGATOR_PORT}

• Open experiment-manager.ipynb in Jupyter Notebook and run all of its cells.

Loss function for experiment with 4 collaborators using distributed system and model from Qu R., et al.

Loss function for experiment with 4 collaborators using simple CNN.

Loss function for experiment with 3 collaborators using simple CNN.

Client_1 train dataset distribution:

Client_1 test dataset distribution:

Client_2 train dataset distribution:

Client_2 test dataset distribution:

Client_3 train dataset distribution:

Client_3 test dataset distribution:

Client_4 train dataset distribution:

Client_4 test dataset distribution: