High-Fidelity Network Management for Federated AI-as-a-Service: Cross-Domain Orchestration

2026-02-16

AI Summary

This work addresses the lack of end-to-end guarantees against communication and inference impairments—such as latency, packet loss, and errors—in multi-domain Federated AI-as-a-Service (AIaaS) environments. The authors propose a guarantee-oriented AIaaS management plane that introduces composable and verifiable Tail Risk Envelopes (TREs), integrating stochastic network calculus with tail risk modeling to enable intent-driven joint orchestration of networking and computing resources and a decomposable end-to-end budget for delay violation probabilities. Coupled with a runtime telemetry-based auditing mechanism, the framework supports cross-domain accountability attribution. Experimental results demonstrate that the approach significantly improves p99.9 latency compliance under overload and bursty traffic conditions while ensuring strong tenant isolation and precise tail-risk accountability.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Distributed Machine Learning & Federated Learning
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

To support the emergence of AI-as-a-Service (AIaaS), communication service providers (CSPs) are on the verge of a radical transformation-from pure connectivity providers to AIaaS a managed network service (control-and-orchestration plane that exposes AI models). In this model, the CSP is responsible not only for transport/communications, but also for intent-to-model resolution and joint network-compute orchestration, i.e., reliable and timely end-to-end delivery. The resulting end-to-end AIaaS service thus becomes governed by communications impairments (delay, loss) and inference impairments (latency, error). A central open problem is an operational AIaaS control-and-orchestration framework that enforces high fidelity, particularly under multi-domain federation. This paper introduces an assurance-oriented AIaaS management plane based on Tail-Risk Envelopes (TREs): signed, composable per-domain descriptors that combine deterministic guardrails with stochastic rate-latency-impairment models. Using stochastic network calculus, we derive bounds on end-to-end delay violation probabilities across tandem domains and obtain an optimization-ready risk-budget decomposition. We show that tenant-level reservations prevent bursty traffic from inflating tail latency under TRE contracts. An auditing layer then uses runtime telemetry to estimate extreme-percentile performance, quantify uncertainty, and attribute tail-risk to each domain for accountability. Packet-level Monte-Carlo simulations demonstrate improved p99.9 compliance under overload via admission control and robust tenant isolation under correlated burstiness.

Problem

• AI-as-a-Service
• multi-domain federation
• high-fidelity
• network orchestration
• tail-risk

Innovation

• Tail-Risk Envelopes
• Federated AI-as-a-Service
• Stochastic Network Calculus
• Cross-Domain Orchestration
• End-to-End Assurance