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AI & Edge Inference

Funion Architecture​

The Echomix paper introduces "funion" — verifiable, composable AI inference that runs on edge devices without cloud dependency.

Core Principles​

  • Local-First Inference — AI models execute on edge hardware, keeping data local and private. No cloud APIs, no data exfiltration.
  • Metadata-Private Communication — AI agents communicate through ZKNetwork's mixnets. Agent-to-agent coordination, model weight updates, and inference requests traverse encrypted, unlinkable paths.
  • Verifiable Outputs — Zero-knowledge proofs verify inference correctness without revealing inputs, model architecture, or intermediate computations.
  • Functional Composition — AI capabilities compose like functional programs — modular, testable, verifiable. Each inference step produces proofs that downstream agents can verify independently.

Edge AI on ZKNetwork Hardware​

ZKNetwork's edge nodes are tamper-resistant devices that run inference locally, participate in the mixnet, and earn ZKN for useful computation.

Hardware Capabilities​

  • zerOS AI Runtime — Hardened Linux-based OS with inference stack, secure enclave key storage, and encrypted model weight protection.
  • Power-Sovereign Inference — Solar/battery compatible edge nodes running AI independently of centralized power.
  • Hardware-Backed Identity — Secure enclaves store model credentials and sign inference proofs. Tamper detection triggers automatic key rotation and zero-knowledge re-attestation.
  • Mesh AI Coordination — Edge nodes form inference meshes — distributing computation, sharing model updates via mixnet, and solving problems without exposing individual data.

Self-Hosted Private Inference Stack​

Stack Components​

  • Local Model Execution — Run LLMs, vision models, and custom inference pipelines entirely on your edge hardware. Models load from encrypted storage, execute in secure enclaves, outputs verified via ZK proofs.
  • Privacy-Preserving APIs — Expose inference as a ZKNetwork service. Clients request inference through mixnet routing, receive verified outputs without revealing their identity, location, or query patterns.
  • Federated Learning with Privacy — Model updates route through mixnets, aggregated with differential privacy, verified via ZK proofs. Global model improvement without exposing local data or participation patterns.
  • Agentic Coordination — AI agents (like Eve from Coasys) run locally, reason privately, communicate via mixnets. Multi-agent problem solving without any participant revealing their role, data, or intent.

Metadata-Private AI Workflows​

Privacy Guarantees​

  • Unlinkable Inference Requests — Requests enter through mixnet entry nodes, traverse multiple relays, exit through random nodes. Sender-receiver unlinkability for every AI query.
  • ZK-PKI for AI Agents — Agents prove their identity, reputation, or credentials without disclosure. Verifiable AI services — prove model version, training data compliance, or inference correctness without revealing proprietary information.
  • ZK-BOM for Model Provenance — Recursive proofs track model lineage, training data sources, and update history. Supply chain integrity for AI — know exactly what you're running and where it came from.
  • Timing Obfuscation — Cover traffic and dummy inference requests hide real query patterns. Even the timing of AI requests becomes indistinguishable from background noise.

AI Tokenomics: Earn ZKN for Computation​

Reward Mechanisms​

  • Inference Rewards — Edge nodes earn ZKN for serving inference requests. Rewards scale with model complexity, compute time, and verifiable output quality. Stable payouts for reliable AI services.
  • Model Hosting Stakes — Host models, stake ZKN as performance bond. Higher stakes unlock priority routing, larger models, enterprise clients. Slashable for downtime or incorrect outputs.
  • Federated Learning Bounties — Contribute local model updates, earn ZKN proportional to update quality and uniqueness. Differential privacy budgets tracked on-chain, rewards distributed automatically.
  • Agent Reputation Staking — AI agents stake ZKN to signal trustworthiness. Successful task completion, verified via ZK proofs, grows reputation and earning capacity. Malicious behavior burns stake.

Implementation Pathway​

Phase 1: Edge Runtime​

zerOS AI stack deployment. Local model execution on tamper-resistant hardware. Basic inference API with mixnet routing for request/response privacy.

Phase 2: Agent Infrastructure​

Coasys Eve integration. AD4M transport adapter for Katzenpost. ZK-PKI for agent identity. Private agent-to-agent coordination protocols.

Phase 3: Verifiable Inference​

ZK proof generation for inference correctness. ZK-BOM for model provenance tracking. Federated learning with differential privacy and mixnet aggregation.

Phase 4: Enterprise AI​

Enterprise-grade inference services. HIPAA/GDPR-compliant AI with ZK verification. Private AI marketplaces where models and compute trade without exposing participants.

See also: Architecture | Hardware | Technology