ChainFL

ChainFL is a modular simulation framework for Federated Learning with blockchain-based trust and model integrity mechanisms. Designed for experimentation, research, and education in secure and decentralized AI.

🚀 Features

• Federated Learning with pluggable aggregation (FedAvg, etc.)
• Blockchain-simulated model registry with PBFT-style consensus
• Agent-based training, hashing, signing, and verification
• Support for Sybil attack simulation and trust audits
• YAML-based configuration and modular architecture

📊 Architecture Diagram

[Simulation] → [Agent Nodes] → [Consensus Engine] → [Blockchain Ledger]
         ↓                        ↑                        ↓
   [Scheduler]        ←    [Coordinator: Aggregator + Publisher]

📁 Project Structure

chainfl/
├── agent/          ← Model training, hashing, signing
├── blockchain/     ← Ledger, consensus, validators
├── coordinator/    ← Aggregation, verification, publishing
├── simulator/      ← Rounds, metrics, scheduler
├── utils/          ← YAML loader, crypto helpers
├── examples/       ← Run scenarios: minimal, sybil attack
├── tests/          ← Pytest unit tests
├── config/         ← default.yaml configuration

📦 Installation

git clone https://github.com/yourname/chainfl.git
cd chainfl
pip install -r requirements.txt

🧪 Run an Example

python examples/minimal_chainfl_run.py

📜 License

MIT License – see LICENSE

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Created by Tymoteusz Miller