Inferix Decentralized GPU
  • Getting Started
    • Overview
    • $IFX
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  • Inferix Whitepaper
    • Introduction
      • Rendering network using crowdsourced GPU
      • Rendering verification problem
    • High-level description of ANGV
      • Noise generation
      • Noise verification
      • Thread model
    • Implementation of ANGV
      • Structure of noise
      • Noise insertion
        • Geometric constraints
        • Distortion region
      • Adaptive noise spreading
      • Verification key generation
      • Noise verification
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        • Attacks on noises
        • Attacks on verifiers
      • Performance evaluation
      • Integration
    • Decentralized visual computing
      • Client Apps plugin
      • Client API and SDK
      • Manager node
      • Worker node
      • Decentralized storage
        • Data categories
        • Multi-level 3D polygon data
        • Polygon digester
        • Decentralized storage
        • Decentralized cache
      • Data security with FHE and TEE
        • Verifier data security enhancement with FHE
        • Worker and Manager data security enhancement with FHE
    • Decentralized federated AI
      • Federated learning with TensorOpera
      • Meta LLaMA
      • Stable Diffusion
      • Other AI models
      • Inferix AI
    • Economic model
      • GPU compute market for visual computing and federated AI
      • Inferix vision
      • $IFX token
      • Burn-Mint-Work token issuance model
      • Inferix bench and IBME
        • IB and IBM
        • IBME
      • Price simulation
      • Token metrics and allocation
        • Token allocation
        • Token vesting
      • Governance
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        • Worker
        • Verifier
        • Manager
        • Penalty pool
      • Node sale and guaranteed node buyback
        • Node sales
        • Guaranteed Node Buyback
    • Future development
      • PoR and NFT minting for graphics creative assets
      • ZKP and PoR communication
      • Inferix RemotePC
      • Rendering professional network
    • References
    • Appendix A: Proofs
    • Appendix B: Price simulation details
    • Appendix C: Hardware requirements for nodes
    • Appendix D: Performance evaluation data
  • Verifier Node Guide
    • What is Verifier Node
      • How do the Verifier Node work
      • Verifier Node Rewards
      • How to run Verifier Node
      • What is the Verifier Node License (NFT)
    • Verifier Node Sales
      • Guide to Purchase Verifier Nodes
      • Verifier Node Sale Timeline
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      • Verifier Node Purchase FAQ
      • Aethir Node Winners FAQ
  • Inferix MVP
    • Tutorial: MVP for designers & GPU owners
    • PoR MVP
  • Inferix Testnet 2 on Solana & IoTeX [LIVE]
    • Adding GPUs to the Network
      • For GPU providers
      • For GPU providers without funds
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    • Renting GPU Devices
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      • Staking Requirements
      • Unstaking GPUs
    • Guide to get tIFX tokens
    • Why choose Inferix DePIN GPU Solutions?
  • Inferix Testnet 1 on IoTeX [ENDED]
    • Inferix GPU Solutions
    • Adding GPUs to the Network
    • Renting GPU Devices
    • User Revenue Calculation
    • GPU Staking
    • Multiple options to participate in the Staking & Mining Program
    • Special airdrop for Inferix Node Holders! 🎉
    • Guide to get tIFX tokens
    • FAQ
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  1. Inferix Whitepaper
  2. Decentralized federated AI

Federated learning with TensorOpera

PreviousDecentralized federated AINextMeta LLaMA

Last updated 8 months ago

Federated learning and its practical benefits have recently started to see widespread application. This article will not delve into the concept of federated learning itself but will focus on applying it to leverage the GPU infrastructure of Inferix.

Several foundational projects have developed tools/SDK for federated learning developers. After extensive evaluation, we have chosen the open-source TensorOpera as the basis for developing the Inferix Federated Learning framework.

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