TRACK: Decentralized AI

NEAR Bounty: Decentralized Federated Learning for Impact-Driven AI

Objective:

Design and develop a proof-of-concept for a decentralized federated learning platform that enables multiple participants to collaboratively train machine learning models without sharing their raw data. The solution should focus on privacy-preserving techniques and aim to address real-world challenges in areas like healthcare, environmental monitoring, or financial inclusion.

Hackathon Challenge:

Focus on creating a decentralized architecture that facilitates federated learning across multiple nodes, ensuring data privacy and security. Integrate incentive mechanisms for data and model contributions, and demonstrate how the platform can be applied to an impact-driven AI use case.

Key Targets for the Hackathon:

• Decentralized Model Training Protocol:
  • Implement a protocol for federated learning that operates over a decentralized network, allowing participants to contribute to model training without exposing their data.
• Privacy-Preserving Techniques:
  • Incorporate methods such as Homomorphic Encryption, Differential Privacy, or Secure Multi-Party Computation to protect sensitive information during the training process.
• Incentive Mechanisms:
  • Design and implement a system to reward participants for contributing data or computational resources, possibly utilizing blockchain-based tokens or smart contracts.
• Integration with Apps:
  • Showcase how your federated learning platform can be integrated into existing Apps to enhance functionality or create new services.
• Governance and Ethical Considerations:
  • Propose a governance model addressing ethical considerations, data ownership, and compliance with regulations like GDPR.

Data Samples & Sources:

• Public Datasets:
  • Utilize datasets relevant to your chosen impact area (e.g., healthcare, environmental data) that are suitable for federated learning.
• Synthetic Data Generation:
  • Create synthetic datasets to simulate distributed data scenarios and test the scalability of your solution.

Tech Stack Recommendations:

• Programming Languages:
  • Python for machine learning components.
  • Solidity or Rust for smart contract development.
• Blockchain Platforms:
  • NEAR for implementing decentralized network features and smart contracts.
• Federated Learning Frameworks:
  • TensorFlow Federated, PySyft, or Flower for building federated learning workflows.
• Privacy Libraries:
  • Use libraries like Microsoft SEAL for Homomorphic Encryption, Google's Differential Privacy library, or Nillion for privacy-preserving computations.