Federated Learning Backend

This repository contains the backend of the web solution developed to facilitate federated learning which can be orchestrated using a web front end.Both client and server code can be found here

Federated Learning

Code for client and server is provided in their respective folders.

Server

dependencies

1. pytorch 2.0 or latest
2. websockets (pip install websockets)
3. torchvision
4. bson (pip install bson)

Download Server code and run wbsocket_server.py.

The data to be tested against for calculating test accuracy during training should be stored in a subfolder in ./data folder.

Client

dependencies

1. pytorch 2.0 or latest
2. websockets (pip install websockets)
3. torchvision

run python client_service.py 5000 (or any number for port) on the client device

The data to train the model should be stored in a subfolder in ./data folder.

Storage service

This section of the code is for managing a separate flask service for storing and downloading the trained model which will help in comparing the trained model on the front-end(user interface)

dependencies

1. psycopg2
2. flask
3. pytorch 2.0 or latest

Posdtgres database needed to be run in order for the storage service to run

Install postgres in your local machine by following the official documentation.

Once postgres is installed create database and tables using the commands found in feddb text file.

Change port and db names accordingly in the file connector.py

Finally go to StorageService folder and run command flask --app app run

Demo

Once all the services (Server, Client, Storage) are running FrontEnd need to be run to create a federated learning job using web Interface. (Instructions found on FedLFrontEnd repo)

Demo for running the web app