Cross-Chain AI Model Interoperability for Secure AI-as-a-Service

Abstract

Cross-chain interoperability represents a transformative paradigm for the secure, scalable delivery of AI-as-a-Service (AIaaS). Traditional blockchain-based AI marketplaces and inference platforms are constrained by single-ledger architectures, leading to fragmentation of model repositories, siloed data governance, and susceptibility to localized failures. In this work, we introduce a comprehensive Cross‑Chain AI Model Interoperability (CAIMI) framework that seamlessly orchestrates AI model publication, discovery, and inference across heterogeneous blockchain networks—specifically Hyperledger Fabric, Ethereum, and Polkadot. Leveraging threshold cryptography, our design ensures that model encryption keys are split among validator nodes, preventing any single point of compromise. Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) furnish a robust, self‑sovereign identity layer, enabling fine‑grained access control without reliance on centralized authorities. A custom cross‑chain relay implements atomic lock‑and‑unlock semantics for model transfers, augmented by off‑chain oracles and Intel SGX–based trusted execution environments for cost‑effective, privacy‑preserving inference. We deploy a ResNet‑50 model over a geographically distributed testbed, achieving sustained publication throughput of 520 transactions per second with end‑to‑end latency under 1.8 seconds, and inference throughput of 480 TPS with latency under 1.2 seconds. Security analysis confirms resistance to collusion, replay, and 51%‑style attacks, while privacy benchmarks demonstrate that model weights remain encrypted and only reconstructable by authorized threshold participants. By unifying cryptographic, identity, and interoperability primitives, CAIMI paves the way for a truly borderless AI marketplace, enhancing trust, resilience, and data sovereignty in next‑generation AIaaS ecosystems.