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fazeZEROApplication Atlas

Demo listing. This page uses illustrative seed metadata to demonstrate the Atlas. Claims shown here are not verified product facts.

Fedbounty

Digital Assets & Virtual Assets

Demo

A federated model-improvement bounty exchange where organisers publish base models and holdout validation criteria, workers train locally on private data, and rewards pay out only on measured validation lift — with anti-poisoning enforced by scoring improvements workers cannot see.

Deployment-Ready FoundationfazeZERO VerifiedProprietary

Last verified 15 Sept 2026 · Updated 23 Sept 2026 · Metadata v1

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Configuration + implementation engagement

Problem

ML organisers (enterprises, research labs, OpenMined-style communities) who rejected on-chain training after DanKu-style cost pain (15MB storage ~270 ETH) and want federated local training with Sonar-like smart-contract rewards tied to validation-set improvement, not self-reported gradients.

Outcome

A deployment-ready foundation that teams can configure into production.

Who uses it

  • Operations Analyst
  • Solution Architect

Key capabilities

  • Core workflow

    AI-assistedIncluded

    Primary operating workflow for this foundation.

  • Document intelligence

    AI-assistedIncluded

    Extract and ground decisions from operating documents.

Typical workflow

Primary operating flow

Trigger: An operator starts the primary use case

  1. Task

    Stage 1: Capture inputs

    Operator

  2. AI-assisted

    Stage 2: Assist with draft

    System

  3. Human checkpoint

    Stage 3: Human approval

    Approver

Task Automated AI-assisted Human checkpoint Decision

About this foundation

Federated bounty settlement. Ivan Kukanov's May 2018 talk fuses AI and blockchain: DanKu protocol markets ML scientists/miners/organisers with Ether rewards but on-chain ML is impractical (storage cost, Solidity math pain); OpenMined combines federated learning, IPFS models, MPC vs homomorphic encryption, and Sonar contracts where users train locally, upload to IPFS, and earn rewards — with hijack resistance because reward is measured on a validation set (% improvement maps to reward). Fedbounty productises that exchange — distinct from Agentfence (agent autonomy control) and Modelescrow (model escrow).

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