Secure Healthcare Data: Fed Learning & Blockchain

Project Title:
Improvising Healthcare Data Security using Federated Learning and Blockchain Framework

Objective:
To develop a secure system for healthcare data privacy and integrity by integrating Federated Learning (FL) and Blockchain technologies.

Key Requirements:

Federated Learning Implementation:

Train ML models locally on IoT devices.
Aggregate model updates on a central FL server.
Blockchain Integration:

Develop a private Blockchain network to validate and store model updates.
Implement smart contracts for ensuring secure transactions.
System Integration:

Ensure seamless communication between FL server, Blockchain, and IoT devices.
Proof of Concept (PoC):

Use a small healthcare dataset to demonstrate functionality.
Security Features:

Ensure data privacy during training.
Prevent unauthorized access or tampering.
Expected Deliverables:

System architecture and design documentation.
Fully functional PoC with basic dataset.
Source code with proper comments.
Deployment instructions and user manual.
Preferred Skills:
Python (TensorFlow, PyTorch).
Blockchain frameworks (Hyperledger/Ethereum).
Knowledge of IoT and ML systems.