Federated AI Infrastructure

This paper introduces a formal architecture for a decentralized, sovereign, and resilient Federated AI Infrastructure. As centralized AI platforms increasingly risk API lock-in and catastrophic single-provider dependencies, this work details an open-source alternative that allows heterogeneous, locally deployed AI models to collaborate without a central authority. The architecture introduces two core components: The Federated Memory Fabric (FMF): A permissioned, distributed context retrieval environment that facilitates selective runtime memory propagation across independent nodes. The FI-13 Protocol Stack: A thirteen-protocol interoperability framework organized across four logical layers (Transport, Semantic Alignment, Reasoning Exchange, and Governance Coordination) rooted in Protocol Zero (P0)—the physical hardware substrate utilizing ultra-low latency Hollow-Core Fiber (NANF) and space-manufactured ZBLAN fluoride glass. A hard constraint of the FI-13 architecture is absolute sovereignty preservation: no participating model or node is required to expose raw internal model weights, training datasets, or unrestricted reasoning traces. Instead, nodes selectively exchange tokenized context deltas and multi-space projection tensors to build a collective, self-verifying machine intelligence. The manuscript includes deep threat modeling against state-level adversarial actors, an empirical proof-of-concept configuration utilizing local edge clusters, and a phased physical substrate deployment roadmap from dark fiber pilots to national infrastructure.