FLAVOR

Federated Learning Analytics, Visualization, Optimization & Reliability

FLAVOR is a tool for visualizing and understanding the privacy-preserving properties and optimization techniques used in federated learning. App live at https://flavor-studio.vercel.app/

Features

• 9 Interactive Sections with 38 total steps
• Shamir Secret Sharing Scheme implementation (7-of-10 threshold)
• FedAvg Algorithm visualization
• MNIST Dataset simulation with non-IID distribution
• Dark/Light Theme toggle
• Keyboard Navigation (Arrow keys, Space)

Sections

1. Intro - What is Federated Learning?
2. Data Distribution - MNIST across 10 clients
3. Local Training - Client-side model training
4. Shamir Secret Sharing - Deep dive into the cryptography
5. Secure Aggregation - Combining shares privately
6. FedAvg - Weighted averaging algorithm
7. Global Model - Model update process
8. Convergence - Multi-round training progress
9. Summary - Results and next steps

Tech Stack

• Next.js 14 (App Router)
• TypeScript
• Tailwind CSS + shadcn/ui
• Framer Motion (animations)
• D3.js (visualizations ready)

Getting Started

# Install dependencies
npm install

# Run development server
npm run dev

# Build for production
npm run build

# Start production server
npm start