AGILE-The Blockchain for AGI
  • Introduction
  • Motivation
  • Core Principles
  • On-Chain AGI Registry
    • On-Chain Decentralized GPU Node Registry
    • On-Chain AI Model Registry
    • On-Chain AI Agent Registry
    • On-Chain AGI Training Data Registry
    • On-Chain Model Creation & Decentralized Training
  • On-Chain GPU Bidding Marketplace
  • AGI Council and Governance Framework
    • AGI Council and Transparent Governance
    • AGILedger Governance: Empowering Transparent & Inclusive AGI Innovation
    • AGI Council: Driving Inclusive and Responsible AGI Governance
    • Robust Security and Compliance
    • Adaptive and Scalable Governance
  • Core Components
    • Blockchain Layer
    • Storage Layer
    • Compute Layer
    • Interface Layer
  • User Experience Flows
  • Architecture
    • Layer-1 Blockchain Foundation
    • Smart Contracts & Protocol Modules
    • Oracles & Layer-2 Extensions
    • Advanced Security & Privacy Measures
  • Use Cases
    • Decentralized AGI Marketplace
    • On-Chain Federated Learning
    • Open-Source Model Collaboration
    • Data-Sharing Platform
    • Incentivized Knowledge Graphs
  • Tokenomics
    • Phase 1: Community Rewards Token
    • Phase 2: $AGILE — The Native Utility Token
  • Conclusion
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  1. On-Chain AGI Registry

On-Chain Model Creation & Decentralized Training

On-Chain Model Creation & Decentralized Training AGILedger elevates blockchain functionality by enabling fully decentralized AI model creation through distributed, trustless training workflows.

  • Distributed Collaborative Training Harnesses a global network of GPU resources, allowing participants to jointly train and refine AI models via blockchain-coordinated processes.

  • Tokenized Incentives Contributors who supply computing power or curated datasets receive transparent, on-chain rewards in AGILedger’s native tokens, spurring sustained platform engagement.

  • Secure Federated Learning Employs encrypted gradient aggregation and other cryptographic techniques, ensuring that private data remains confidential throughout collaborative model development.

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