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

Secure Transmission of Encrypted Updates

To securely share encrypted model updates between each node and Unity Network’s central server after each epoch, using Elliptic Curve Diffie-Hellman (ECDH) for key exchange. This ensures that updates are secure in transit, allowing for privacy-preserving aggregation and continuation of training for additional epochs.

Process Overview

  1. ECDH Key Exchange: Establishes a unique shared encryption key between each node and the central server.

  2. Model Update Encryption: Each node encrypts its model updates using the shared key derived from ECDH.

  3. Transmission of Encrypted Model Updates: Encrypted updates are sent to the central server.

  4. Secure Aggregation and Decryption: The central server aggregates encrypted updates, decrypts the result, and sends the aggregated model back to nodes.

  5. Continuation of Training: The updated model is distributed back to each node for the next epoch of training.

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Last updated 5 months ago