Unity Network
  • Unity Network
    • Introduction
    • Unity Network FL Architecture
    • Data Security
    • Case Study
      • Flashback Overview
      • Registration
      • Integration
      • Model Training
      • Objectives and Outcomes
  • Unity Network SDK
    • Key Features
    • Registration
    • Node Setup
    • SDK Integration
      • Wallet Module
      • Node Module
  • Permissions Management
    • User Permissions
  • Model Training
    • Organization registration
    • Model Training Requests
    • Secure Training and Updates
      • Model Training
        • Model Loading and Initialization
        • Data Loading
        • Training
      • Secure Transmission of Encrypted Updates
        • ECDH Key Exchange for Secure Encryption Key Generation
        • Encrypt and Transmit Model Updates
        • Secure Aggregation and Decryption at Central Server
        • Distribute Updated Model and Continue Training
      • Sharing Model Updates with the Model Owner and Verifying Authenticity of Training
        • Construct the Merkle Tree and Commit to the Merkle Root
        • Log Hashes of Accessed Dataset Chunks During Training
        • Transmission of Model Updates, Merkle Proofs, and Hash Log to the Model Owner
        • Verification by the Model Owner
  • Training rewards
    • Incentivization Process
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  1. Model Training
  2. Secure Training and Updates
  3. Sharing Model Updates with the Model Owner and Verifying Authenticity of Training

Construct the Merkle Tree and Commit to the Merkle Root

  1. Dataset Hashing and Chunking:

    • SDK on each node divides its dataset into smaller chunks (e.g., rows or images) and hashes each chunk. These hashes are used as the leaf nodes of the Merkle Tree.

  2. Building the Merkle Tree:

    • The node pairs up the chunk hashes and hashes each pair to build the next level of the tree.

    • This process is repeated until a single Merkle root is reached, which serves as a unique fingerprint for the dataset.

  3. Commit the Merkle Root to the Model Owner:

    • Each node shares its Merkle root with the model owner before training begins. This root serves as a reference for later verification, ensuring that only the committed dataset is used in training.

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Last updated 5 months ago