Inferix Decentralized GPU
  • Overview
  • Tokenomics
  • Introduction
    • Rendering network using crowdsourced GPU
    • Rendering verification problem
  • 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
  • Inferix Testnet 1
    • Inferix GPU Solutions
    • Adding GPUs to the Network
    • Renting GPU Devices
    • GPU Staking
  • Future development
    • PoR and NFT minting for graphics creative assets
    • ZKP and PoR communication
    • Inferix RemotePC
    • Rendering professional network
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  1. Decentralized visual computing
  2. Decentralized federated AI

Federated learning with TensorOpera

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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Last updated 4 months ago