Model Hub – Decentralized Machine Learning Model Marketplace

Model Hub is a decentralized platform designed to make machine learning models more accessible, secure, and fairly monetized.
It allows creators to upload encrypted models, and users can discover, rent, and use them through a transparent and trust-driven system powered by blockchain and decentralized storage.

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⭐ Key Features

• Decentralized Model Marketplace
  Creators can upload ML models and earn credits for each prediction request.

• AES-Encrypted Model Uploads
  All models are encrypted before storage to ensure privacy and prevent misuse.

• IPFS Storage (Pinata Integration)
  Encrypted models are stored on IPFS for decentralized, tamper-proof availability.

• Smart Contracts on Arbitrum Stylus (Sepolia)
  Rust-based smart contracts manage payments, credits, and access control.

• Secure API Access with HMAC Authentication
  Users receive API keys that verify each prediction request.

• Mini Zero-Knowledge Verifiable Proofs
  Predictions can be verified without revealing sensitive data or the model itself.

• Python Inference Microservice
  Handles secure model loading, prediction, and temporary decryption.

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🧩 System Architecture

Below is the architecture diagram:

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🏗️ Tech Stack

Frontend

• React.js
• Tailwind CSS
• MetaMask / Web3 wallet integration

Backend

• Node.js
• Express.js
• MongoDB (stores encrypted keys, user metadata)

Blockchain

• Rust-based smart contracts
• Arbitrum Stylus (Sepolia Testnet)

AI & Inference

• Python
• Secure inference microservice

Storage

• IPFS (Pinata)

Security

• AES Encryption
• HMAC-SHA256 API Authentication
• Zero-Knowledge Proofs for prediction verification

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🔄 Workflow Overview

1. Creator uploads an ML model through the frontend.
2. Model is AES-encrypted and stored on IPFS.
3. Smart contract registers the model with pricing and ownership.
4. User purchases credits via MetaMask (on Stylus).
5. User makes a prediction request using their HMAC API key.
6. Backend fetches the encrypted model, decrypts temporarily, and runs Python inference.
7. A zero-knowledge proof is generated to validate prediction authenticity.
8. User receives the prediction result.

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🚀 Future Enhancements

• Cross-chain support for more networks
• Homomorphic encryption & zkML for privacy-preserving inference
• Community governance and reward mechanisms
• Decentralized compute layer for large model execution

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📜 License

This project is licensed under the MIT License.

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📧 Contact

For queries or collaboration, feel free to reach out.