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.

翻译：分布式人工智能与物联网应用日益在跨越终端设备、边缘/雾基础设施及云平台的异构资源上执行，这些资源常隶属于不同管理域。流态计算作为一种新兴范式，通过将此类资源视为统一的计算织物，依据应用需求驱动实现与具体服务无关的最优部署，从而增强跨计算连续体的大规模资源管理能力。然而，现有解决方案在很大程度上仍采用集中式架构，且通常未明确考虑多域管理问题。本文提出一种面向流态计算环境的、与具体技术无关的多域编排架构。该编排平面支持各管理域在保持本地自治的同时进行去中心化协同，共同实现租户基于意图的部署请求，确保端到端的服务放置与执行。为此，该架构将域侧控制服务提升为一等能力，以支持运行时应用级功能增强。作为一个代表性应用案例，我们研究了拜占庭威胁下的多域去中心化联邦学习部署。通过利用域侧能力增强拜占庭安全性，我们提出了FU-HST——一种支持软件定义网络的多域异常检测机制，该机制可与拜占庭鲁棒聚合方法形成互补。我们通过在单域与多域场景下的仿真验证了该方案，评估了异常检测性能、去中心化联邦学习效果及计算/通信开销。