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Decentralized AI Training

A federated node infrastructure that distributes AI training tasks across idle GPUs and CPUs within the Hednet network. Enable on-device learning, real-time inference, and privacy-preserving computation.

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How it solves real-world problems:​

  • Cuts AI Costs: Removes the barrier of expensive GPU clusters.

  • Preserves Privacy: Federated learning means data stays local; no central server holds sensitive info.

  • Reduces Energy Use: Utilizes hardware already running elsewhere.

Key Benefits:​

  • GPU-Accelerated Training: Harness idle GPUs across the network to train complex AI models without centralized infrastructure.

  • CPU-Orchestrated Workflows: Offload orchestration, data preprocessing, and federated learning coordination to distributed CPUs.

  • Scalable AI Infrastructure: Dynamically scale across distributed nodes based on workload demands.

  • Real-Time Inference at the Edge: Deploy models close to where data is generated—reducing latency and bandwidth usage.

Encrypted Computation: Ensure privacy and integrity using zero-knowledge proofs (ZKPs) and secure execution environments.