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

Organization registration

Model Owner Registration on Unity Portal

  1. Create an Account:

    • Model owners create an account on the Unity portal, providing essential details such as organization name, type, contact information, and purpose for joining the network.

    • Similar to data organizations, model owners are required to undergo verification, which includes validating organizational identity and intended model use cases.

  2. Model Registration:

    • After account creation, model owners can register their models on the Unity portal. During model registration, they provide:

      • Model Details: A description of the model’s purpose and functionality, such as image classification, facial recognition, or feature extraction.

      • Training Requirements: Specific requirements like data type, minimum dataset size, and training parameters needed for successful training.

    • Once the model is registered, Unity performs compatibility checks to ensure it aligns with network protocols and privacy standards.

  3. Verification and Access to Datasets:

    • Upon successful verification of the model owner and model, the Unity portal displays available datasets from verified data organizations.

    • Each dataset includes a summary (e.g., data type, sample size, and permissions required) to help model owners assess suitability for their training needs.

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Last updated 5 months ago