FedChain 🚀

A privacy-preserving federated-learning system secured by an Ethereum blockchain.
Clients train on disjoint MNIST shards, submit hashed model updates via Solidity smart contracts, and store both submissions and the aggregated global model on-chain.

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🔧 Prerequisites

• Git
• Docker & Docker Compose
• A local Ethereum network (we use Ganache CLI)

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⚙️ Setup & Run

1. Clone the repo

   git clone https://github.com/furk4neg3/FedChain.git
   cd FedChain

2. Create your .env

   SERVER_IP="XXX.XXX.XXX.XXX"

   Find your IP via:

   • Windows: ipconfig
   • macOS/Linux: ifconfig or hostname -I

3. Build & start everything

   docker-compose up --build

   • 🚨 Heads-up: The first build can take over 1 hour, as it builds images and runs 3 rounds of local training + global aggregation on MNIST.

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📊 What’s Happening Under the Hood

1. Local Training
   • 3 clients each train a small CNN on their shard (C1: digits 0–2; C2: 3–6; C3: 7–9).
   • Local accuracies: ~30 %, ~40 %, ~30 %.
2. Submission
   • Each client serializes its weights → computes a Keccak hash → calls submitModel(hash, accuracy) on the smart contract.
3. On-Chain Logging
   • Solidity contract (on Ganache) logs (round, client, hash, accuracy) in a new block.
4. Server Aggregation
   • Flask+Web3.py service listens for submission events → fetches off-chain weights → runs Federated Averaging → ~85 % global accuracy → calls updateGlobalWeights(...).
5. Dashboard Updates
   • Flask + Socket.IO pushes real-time charts (Chart.js) showing local vs. global accuracy and block/transaction metadata.

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👀 View the HTML Report

After the containers are up & training finishes, open:

macOS

open logs/report.html

Linux

xdg-open logs/report.html

Windows PowerShell

start .\logs\report.html

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Happy federating! 🤝🔒