OpenPad AI
  • ☀️OVERVIEW
    • Introduction
  • ☀️PROTOCOL
    • Overview
    • System Architecture
    • OPAD Nodes
      • Overview
      • How to become OPAD Node Operators
      • OPAD Verifier Node Sale
      • Rewards
      • Buy-back Program
      • Explorer
    • AI2Earn
    • OPAD Intel Key NFTs
    • Node Disclaimer
    • OpenVerse
  • ☀️AI analytics PORTAL
    • AI Analytics Portal
    • AI Assistant
  • ☀️AI Launchpad
    • Apply to Launch
    • IDO Rules
      • Shielded Rules
      • Riskless Rules
    • KYC Guide
    • How to join IDOs
    • How to stake for buying IDOs
    • Tier System
  • ☀️OPAD Token
    • OPAD Utility Token
    • Tokenomic
    • Buyback & Burn Mechanism
    • Roadmap
  • ☀️Programs
    • Membership Program
    • Ambassador Program
    • Incubation Program
  • ☀️MISC
    • Ecosystem Partners
    • Terms & Conditions
    • Contact Us
    • Communities
      • Openpad AI Website
      • Openpad AI Twitter
      • Openpad AI Telegram Channel
      • Openpad AI Telegram Group
      • Openpad AI Discord
      • Openpad AI Zealy
      • Openpad AI Galxe
      • Openpad AI Youtube
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  1. PROTOCOL

Overview

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Last updated 6 months ago

OPAD Protocol is an AI modular data access layer for Web3 DApps, designed to facilitate seamless user interactions across major blockchain ecosystems.

By prioritizing privacy, and ownership, OPAD Protocol empowers users to engage confidently while generating value from their data. This innovative approach ensures that data-driven interactions are efficient and equitable, fostering a thriving ecosystem for developers and users alike.

The $OPAD token economy encourages users to support data sharing and monetization while ensuring privacy and control for data providers.

Problem we solve

  • Users and developers need to connect to blockchains to build and access crypto applications.

  • Machine learning models are developed by analyzing large amounts of publicly available text and data. However, a small number of individuals currently maintain control and ownership of this data.

  • Traditional machine learning still faces issues related to single-point server failures, which often arise from centralized servers used for global model aggregation. Moreover, it lacks an incentive system, resulting in insufficient participation from local devices in global model training.

  • It is essential to design these systems in a way that enables individuals to protect their privacy rights if they choose to share their data.

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