https://github.com/amanpriyanshu/fl-interactive-game

FL-Interactive-Game: Interactive web game that teaches basic components of Federated Learning
https://github.com/amanpriyanshu/fl-interactive-game

computer-vision decentralized-learning distributed-training federated-learning federated-learning-framework fl game interactive-visualizations machine-learning mnist neural-network neural-networks privacy privacy-enhancing-technologies privacy-protection tensorflow tensorflowjs

Last synced: 6 days ago
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FL-Interactive-Game: Interactive web game that teaches basic components of Federated Learning

• Host: GitHub
• URL: https://github.com/amanpriyanshu/fl-interactive-game
• Owner: AmanPriyanshu
• License: mit
• Created: 2024-11-24T00:54:30.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-11-24T06:04:28.000Z (11 months ago)
• Last Synced: 2025-09-30T10:52:26.694Z (19 days ago)
• Topics: computer-vision, decentralized-learning, distributed-training, federated-learning, federated-learning-framework, fl, game, interactive-visualizations, machine-learning, mnist, neural-network, neural-networks, privacy, privacy-enhancing-technologies, privacy-protection, tensorflow, tensorflowjs
• Language: HTML
• Homepage: https://amanpriyanshu.github.io/FL-Interactive-Game/
• Size: 18 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 🎮 Federated Learning Interactive Game



    

    



Checkout the game at: [FL-Interactive-Game](https://amanpriyanshu.github.io/FL-Interactive-Game/)

An interactive web-based demonstration that makes learning about Federated Learning fun! Train models across multiple clients, experiment with different parameters, and watch your global model improve - all without sharing raw data.

## 🌟 Features

- **Interactive Client Management**: Control 5 independent clients training on MNIST data

- **Real-time Visualization**: Watch training progress with live accuracy plots

- **Configurable Parameters**: 

  - Per-client learning rates and epoch counts

  - Communication dropout probability

  - Data distribution settings (IID vs Non-IID)

- **Model Aggregation**: Combine client models into a stronger global model

- **Responsive Design**: Works seamlessly on both desktop and mobile devices

## 🎯 Learning Objectives

This game helps you understand:

- How Federated Learning works in practice

- Impact of different learning rates and training durations

- Effects of communication dropouts in distributed learning

- Differences between IID and Non-IID data distributions

- Model aggregation and its effects on global performance

## 🎲 How to Play

1. Choose your data distribution (IID or Non-IID)

   - IID: Balanced data

   - Non-IID: Chaos mode

   - *Remember to refresh when switching - those sneaky old models like to hang around! 🔄*

2. Configure each client:

   - Adjust learning rates (0.00001 to 0.001)

   - Set training epochs (5 to 15)

3. Set global parameters:

   - Adjust communication dropout probability

   - This simulates real-world network conditions

4. Start training:

   - Train clients individually or all at once

   - Watch the accuracy plots evolve

   - Aggregate models to improve global performance

## 🔧 Technical Details

- Built with TensorFlow.js

- Uses MNIST dataset (preprocessed for FL setting)

- Neural Network Architecture:

  - Input Layer: 784 neurons (28x28 images)

  - Hidden Layer: 64 neurons, ReLU activation

  - Output Layer: 10 neurons, Softmax activation

## 🏆 Challenge Goals

1. Beat 90% accuracy on the global model

2. Achieve consistent performance across all clients

3. Maintain good performance with high dropout rates

4. Master both IID and Non-IID scenarios

## Some Known Issues

- Model states persist between data distribution switches (refresh required)

- Some mobile devices may experience performance lag with multiple clients

- High learning rates can cause training instability