Federated AI Infrastructure with Verifiable Storage and ESG Integration

Abstract

Centralised AI infrastructure scales but conflicts with latency, auditability, and energy constraints. This paper sets the objective to specify and analyse a Federated AI Infrastructure that aligns with regulatory and ESG commitments while remaining financeable for private operators. The design separates centralised training from decentralised inference and storage across five node classes (μ, S, M, L, XL), coordinated by a verifiable orchestrator and a permissioned DAG implementing asynchronous Byzantine fault tolerance. An incentive model ties a size-neutral availability floor to tiered workload rewards, applying bounded multipliers for service-level attainment, ESG performance, and anti-concentration. Formal optimisation spans investor allocation, congestion-aware routing, and policy instruments, yielding equilibrium conditions for mixed-class participation. Compliance is developed against the Swiss regime, including token-to-fiat conversion through a regulated issuer under FINMA or an equivalent national authority, and aligned with EU frameworks such as the GDPR and ISO 27001. Results indicate a resilient mixed fleet: L and XL nodes concentrate on throughput-intensive inference and ledger validation, while μ to M nodes provide edge inference, storage, and continuous DAG activity. Anti-concentration terms and ESG-adjusted pricing sustain node diversity without material efficiency loss. Implementation depends on trusted metering, on-chain attestations, and posted pricing calibrated to observed queue depths. Limitations include parameter identification, metering fidelity, and jurisdiction-specific licensing.