Decentralized Orchestration Architecture for Fluid Computing: A Secure Distributed AI Use Case

TL;DR

This paper introduces a decentralized multi-domain orchestration architecture for fluid computing environments, enhancing distributed AI deployment security and efficiency across heterogeneous resources.

Contribution

It proposes an agnostic multi-domain orchestration framework with domain control services and introduces FU-HST, a multi-domain anomaly detection mechanism for Byzantine threat mitigation in federated learning.

Findings

Effective decentralized coordination among domains.

Enhanced Byzantine security with FU-HST detection.

Validated performance improvements via simulation.

Abstract

Distributed AI and IoT applications increasingly execute across heterogeneous resources spanning end devices, edge/fog infrastructure, and cloud platforms, often under different administrative domains. Fluid Computing has emerged as a promising paradigm for enhancing massive resource management across the computing continuum by treating such resources as a unified fabric, enabling optimal service-agnostic deployments driven by application requirements. However, existing solutions remain largely centralized and often do not explicitly address multi-domain considerations. This paper proposes an agnostic multi-domain orchestration architecture for fluid computing environments. The orchestration plane enables decentralized coordination among domains that maintain local autonomy while jointly realizing intent-based deployment requests from tenants, ensuring end-to-end placement and execution. To this end, the architecture elevates domain-side control services as first-class capabilities to support application-level enhancement at runtime. As a representative use case, we consider a multi-domain Decentralized Federated Learning (DFL) deployment under Byzantine threats. We leverage domain-side capabilities to enhance Byzantine security by introducing FU-HST, an SDN-enabled multi-domain anomaly detection mechanism that complements Byzantine-robust aggregation. We validate the approach via simulation in single- and multi-domain settings, evaluating anomaly detection, DFL performance, and computation/communication overhead.