Quantum-AI Federated Clouds: A trust-aware framework for cross-domain observability and security

The convergence of quantum computing, artificial intelligence (AI), and federated cloud architecture offers transformative potential for secure, scalable, and privacy-preserving data processing. Yet, trust management and cross-domain observability remain major challenges, particularly in decentralized, heterogeneous cloud environments. This paper introduces Quantum-AI Federated Clouds (QAIFC) a novel trust-aware framework that combines quantum-safe encryption, federated machine learning, and explainable AI to enable secure and observable operations across cloud domains. We present QFedSecure, a protocol suite leveraging lattice-based cryptography, quantum key distribution, and AI-driven anomaly detection to support trust propagation and policy enforcement. The framework features a dynamic trust model, observability protocol, and mechanisms for adversarial resilience. Simulations using Qiskit, TensorFlow Federated, and NS3 show up to 40% improvement in trust calibration and 55% increase in adversarial detection over baseline systems. This work advances the foundation for resilient, decentralized, and quantum-secure AI cloud ecosystems.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-31
DOI
https://doi.org/10.5281/zenodo.17358868
Primary Topic
Architecture, Modernity, and Design
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Quantum-AI Federated Clouds: A trust-aware framework for cross-domain observability and security

Zenodo (CERN European Organization for Nuclear Research)
Architecture, Modernity, and Design
article

Quantum-AI Federated Clouds: A trust-aware framework for cross-domain observability and security

article en

Abstract

The convergence of quantum computing, artificial intelligence (AI), and federated cloud architecture offers transformative potential for secure, scalable, and privacy-preserving data processing. Yet, trust management and cross-domain observability remain major challenges, particularly in decentralized, heterogeneous cloud environments. This paper introduces Quantum-AI Federated Clouds (QAIFC) a novel trust-aware framework that combines quantum-safe encryption, federated machine learning, and explainable AI to enable secure and observable operations across cloud domains. We present QFedSecure, a protocol suite leveraging lattice-based cryptography, quantum key distribution, and AI-driven anomaly detection to support trust propagation and policy enforcement. The framework features a dynamic trust model, observability protocol, and mechanisms for adversarial resilience. Simulations using Qiskit, TensorFlow Federated, and NS3 show up to 40% improvement in trust calibration and 55% increase in adversarial detection over baseline systems. This work advances the foundation for resilient, decentralized, and quantum-secure AI cloud ecosystems.

Zenodo (CERN European Organization for Nuclear Research)
University of Fairfax (US)
Life in Land
Openalex Percentile: Top 38%
Architecture, Modernity, and Design
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.