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Evaluation Method Federated Learning Data Integrity Constraints
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Abstract
Federated computing (FC) enables collaborative computation such as machine learning, analytics, or data processing across distributed organizations keeping raw data local. Built on four architectural pillars, distributed data assets, federated services, standardized APIs, and decentralized services, FC supports sovereignty-preserving collaboration. However, federated systems spanning organizational and jurisdictional boundaries lack a portable mechanism for enforcing sovereignty-critical constraints. They often depend on runtime policy evaluation, shared trust infrastructure, or institutional agreements that introduce coordination overhead and provide limited cryptographic assurance. Federated Computing as Code (FCaC) is a declarative architecture that addresses this gap by compiling authority and delegation into cryptographically verifiable artifacts rather than relying on online policy interpretation. Boundary admission becomes a local verification step rather than a policy decision service. FCaC separates constitutional governance from procedural governance. Admission is validated locally at execution boundaries using proof-carrying capabilities, while stateful services may still implement post-admission controls such as ABAC, risk scoring, quotas, and workflow state. FCaC introduces Virtual Federated Platforms (VFPs), which combine Core, Business, and Governance contracts through a cryptographic trust chain: Key Your Organization (KYO), Envelope Capability Tokens (ECTs), and proof of possession (PoP). We demonstrate the approach in a proof-of-concept cross-silo federated learning workflow using MNIST as a surrogate workload to validate the admission mechanisms and release an open-source implementation showing envelope issuance, boundary verification, and envelope-triggered training.