https://github.com/sharma-anee/federated_learning

This repo is about federated learning implementation using FLOWER framework for beginners.
https://github.com/sharma-anee/federated_learning

deep-learning deeplearning-framework federated-learning federated-learning-examples federated-learning-framework mnist-dataset tensorflow tensorflow-examples

Last synced: 5 months ago
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This repo is about federated learning implementation using FLOWER framework for beginners.

• Host: GitHub
• URL: https://github.com/sharma-anee/federated_learning
• Owner: sharma-anee
• Created: 2022-10-12T05:51:33.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-10-12T07:08:12.000Z (over 3 years ago)
• Last Synced: 2025-02-13T18:49:36.542Z (over 1 year ago)
• Topics: deep-learning, deeplearning-framework, federated-learning, federated-learning-examples, federated-learning-framework, mnist-dataset, tensorflow, tensorflow-examples
• Language: Python
• Homepage:
• Size: 4.88 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Federated_Learning implementaion using Flower(1.0.0). 

This implementation is for beginners using the Flower framework. Here, the MNIST dataset and a simple Deep Learning framework is used.

The scenario in here includes a server and two clients.

Each client having different distribution of data (Non-IID data).

FedAvg algorithm is used as the aggregation method on the server side.

Clone this repo using : git clone https://github.com/sharma-anee/Federated_Learning.git or else copy these codes into your own python editor and save into three seperate files as named here.

Steps to execute these codes:

1. Open the command prompt, move into the exact directories wherever your files are and execute the server.py file using : python server.py.

2. Open another command prompt (a new one), and execute the client1.py file using : python client1.py.

3. Repeat the Step 2. using : python client2.py.

4. Close the graphs generated by step 2 and step 3.

5. You shall see the training and updated model parameters in each of the server and client command prompts window.







Note:  Make sure you have a stable internet connection.

To understand how it actually works and basics of the mechanism, one must go through https://flower.dev/docs/.