Our Roadmap

Zektra — The ZK Compute Factory for Private Model Training.

Zektra Roadmap

Phase 1 — Foundation of the ZK Compute Factory

Goal: Build the core cryptographic and compute infrastructure.

  • Deploy core modules: Homomorphic Encryption Layer, ZK Training Prover, Data Privacy Sandbox

  • Release SDK for encrypted model training jobs

  • Onboard early node operators for encrypted compute validation

  • Integrate basic model frameworks (Regression, CNNs, Transformers)

  • Publish open documentation + academic technical report

  • Community incentives for stress-testing encrypted compute pipelines

Outcome: A fully functioning test network where users can train AI models on private data without exposing raw datasets.

Phase 2 — Encrypted Compute Network Expansion

Goal: Scale the decentralized compute mesh and enable marketplace activity.

  • Launch Zektra Testnet V1

  • Introduce dual staking system:

    • Compute Stakers: provide encrypted compute nodes

    • Security Stakers: stake ZEKTRA to verify ZK proofs

  • Add multi-framework training support (PyTorch, TensorFlow, JAX)

  • Enable encrypted dataset uploads with multi-party data ownership

  • Deploy cross-chain bridges to Ethereum L1 + major L2s

  • Begin data provider onboarding (healthcare orgs, financial firms, quant funds)

Outcome: A decentralized private AI compute layer capable of handling production-level encrypted training tasks.

Phase 3 — The Private AI Marketplace

Goal: Turn Zektra into the world’s first encrypted data-driven AI marketplace.

  • Launch Zektra AI Marketplace for buying/selling ZK-verified model outputs

  • Launch Zektra Mainnet V1

  • Monetization modules for data owners—earn from model training without revealing data

  • Introduce “ZK Trust Score” for compute nodes & model correctness

  • Support collaborative encrypted model training (federated + homomorphic hybrid)

  • Expand privacy tooling: secure inference, encrypted fine-tuning, private RL

  • Partnerships with Web2 & Web3 enterprises for privacy-preserving AI contracts

Outcome: A thriving ecosystem where private data becomes a revenue-generating digital asset.

Phase 4 — Autonomous ZK Training Economy

Goal: Full autonomy, global scale, and universal encrypted AI compute.

  • Launch Zektra DAO for governance and protocol upgrades

  • Introduce self-optimizing encrypted compute clusters

  • Cross-chain deployment across modular rollups / AI-optimized subnets

  • Privacy-preserving autonomous agents using encrypted on-chain training

  • Real-world integrations: hospitals, trading systems, government data labs

  • Advanced privacy primitives: lattice-proof acceleration, FHE-optimized ASICs

  • Global ZK Training Network capable of scaling to millions of encrypted jobs

Outcome: Zektra becomes the world’s default engine for training AI on sensitive data—fully private, mathematically verified, and decentralized.

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