Hierarchical Quantum-Accelerated Federated Learning for Scalable, Auditable Cross-Enterprise AI Governance
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| Natura: | Recurso digital |
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2025
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| _version_ | 1866902071891984384 |
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| author | Sarang Vehale Ruchita Vehale |
| author_facet | Sarang Vehale Ruchita Vehale |
| contents | <p>Traditional federated learning (FL) frameworks face critical challenges in privacy, scalability, and auditability when<br>deployed across multiple enterprises with strin- gent regulatory requirements. Quantum-secure protocols such as Quantum Key<br>Distribution (QKD) and post-quantum cryptography can harden communica- tion channels against both classical and emerging<br>quantum attacks. Meanwhile, variational quantum algorithms (VQAs) promise computational speedups for high-dimensional<br>aggregation tasks that become bottlenecks in large-scale FL systems. We propose a hierarchical, multi-tier Quantum-Federated<br>Learning (QFL) architecture in which local enterprises perform classical model training, regional “quantum hubs” execute<br>VQA-accelerated aggregation and anomaly detection, and a global coordinator enforces UN/ISO AI governance via verifiable<br>zero-knowledge proofs (ZKPs). By bounding quantum resource usage to interme- diate nodes and combining QKD on backbone<br>links with lattice-based encryption at the edge, our design achieves near-term implementability, cost-effectiveness, and end-toend privacy guarantees. Preliminary simulations demonstrate that the proposed scheme reduces communication overhead by<br>over 60% and resists gradient-poisoning attacks with negligible impact on model accuracy. This work lays the foundation for a<br>globally scalable, audit-ready AI governance ecosystem suitable for international deployments</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17441935 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Hierarchical Quantum-Accelerated Federated Learning for Scalable, Auditable Cross-Enterprise AI Governance Sarang Vehale Ruchita Vehale Quantum Federated Learning (QFL), Hierarchical FL, Variational Quantum Algorithms (VQAs), Quantum Key Distribution (QKD), Post-Quantum Cryptography, Zero-Knowledge Proofs (zkSNARKs), AI Governance, Secure Aggregation, Anomaly Detection. <p>Traditional federated learning (FL) frameworks face critical challenges in privacy, scalability, and auditability when<br>deployed across multiple enterprises with strin- gent regulatory requirements. Quantum-secure protocols such as Quantum Key<br>Distribution (QKD) and post-quantum cryptography can harden communica- tion channels against both classical and emerging<br>quantum attacks. Meanwhile, variational quantum algorithms (VQAs) promise computational speedups for high-dimensional<br>aggregation tasks that become bottlenecks in large-scale FL systems. We propose a hierarchical, multi-tier Quantum-Federated<br>Learning (QFL) architecture in which local enterprises perform classical model training, regional “quantum hubs” execute<br>VQA-accelerated aggregation and anomaly detection, and a global coordinator enforces UN/ISO AI governance via verifiable<br>zero-knowledge proofs (ZKPs). By bounding quantum resource usage to interme- diate nodes and combining QKD on backbone<br>links with lattice-based encryption at the edge, our design achieves near-term implementability, cost-effectiveness, and end-toend privacy guarantees. Preliminary simulations demonstrate that the proposed scheme reduces communication overhead by<br>over 60% and resists gradient-poisoning attacks with negligible impact on model accuracy. This work lays the foundation for a<br>globally scalable, audit-ready AI governance ecosystem suitable for international deployments</p> |
| title | Hierarchical Quantum-Accelerated Federated Learning for Scalable, Auditable Cross-Enterprise AI Governance |
| topic | Quantum Federated Learning (QFL), Hierarchical FL, Variational Quantum Algorithms (VQAs), Quantum Key Distribution (QKD), Post-Quantum Cryptography, Zero-Knowledge Proofs (zkSNARKs), AI Governance, Secure Aggregation, Anomaly Detection. |
| url | https://doi.org/10.5281/zenodo.17441935 |