Decentralized Orchestration Architecture for Fluid Computing: A Secure Distributed AI Use Case
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arXiv
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| Format: | Preprint |
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2026
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| author | Cajaraville-Aboy, Diego Fernández-Vilas, Ana Díaz-Redondo, Rebeca P. Fernández-Veiga, Manuel Picallo-López, Pablo |
| author_facet | Cajaraville-Aboy, Diego Fernández-Vilas, Ana Díaz-Redondo, Rebeca P. Fernández-Veiga, Manuel Picallo-López, Pablo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12001 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Decentralized Orchestration Architecture for Fluid Computing: A Secure Distributed AI Use Case Cajaraville-Aboy, Diego Fernández-Vilas, Ana Díaz-Redondo, Rebeca P. Fernández-Veiga, Manuel Picallo-López, Pablo Distributed, Parallel, and Cluster Computing Machine Learning 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. |
| title | Decentralized Orchestration Architecture for Fluid Computing: A Secure Distributed AI Use Case |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2603.12001 |