Quantum-Enhanced Federated Chaotic Transformer Framework for Ultra-Low Latency and Secure Multi-Site Telesurgery in 6G- Enabled Networks Article Swipe
P. Sundaravadivel
,
Augustian Isaac R
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7363876/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7363876/v1
The next generation of telesurgery demands quantum-resilient, ultra-low latency, and highly secure distributed AI frameworks capable of functioning across heterogeneous clinical sites. Building on prior federated learning-based telesurgery frameworks, this research introduces a Quantum-Enhanced Chaotic Gated Transformer Learning (Q-CGTL) architecture designed for real-time, multi-site surgical procedures in 6G-enabled smart hospital environments. The framework integrates (i) quantum random number generators (QRNGs) for entropy-rich key generation in the chaotic encryption layer, (ii) multi-head chaotic gated transformer (CGT) models optimized for multi-modal data streams (haptics, imaging, vitals), and (iii) 6G spectrum slicing with AI-driven latency control, reducing end-to-end feedback latency to under 1 ms. Additionally, a cross-institutional federated transfer learning module enables privacy-preserving model sharing without raw data exposure. Extensive evaluations using a synthetic Surgical Digital Twin dataset and the CIC-IDS-2023 cyber-attack dataset demonstrate a 99.4% intrusion detection accuracy, a 35% reduction in energy consumption, and near-zero packet loss under adversarial conditions. Statistical security assessments, including NIST entropy tests and post-quantum cryptographic analysis, confirm the robustness of the proposed system against quantum adversaries. This research represents a pivotal advancement towards autonomous, secure, and explainable telesurgery ecosystems capable of scaling across global clinical networks.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-7363876/v1
- https://www.researchsquare.com/article/rs-7363876/latest.pdf
- OA Status
- gold
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413800999
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413800999Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-7363876/v1Digital Object Identifier
- Title
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Quantum-Enhanced Federated Chaotic Transformer Framework for Ultra-Low Latency and Secure Multi-Site Telesurgery in 6G- Enabled NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-28Full publication date if available
- Authors
-
P. Sundaravadivel, Augustian Isaac RList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-7363876/v1Publisher landing page
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https://www.researchsquare.com/article/rs-7363876/latest.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-7363876/latest.pdfDirect OA link when available
- Concepts
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Computer science, Chaotic, Computer network, Ultra low power, Quantum, Latency (audio), Distributed computing, Telecommunications, Physics, Power (physics), Power consumption, Artificial intelligence, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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14Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.post-quantum | 158 |
| abstract_inverted_index.Additionally, | 102 |
| abstract_inverted_index.cryptographic | 159 |
| abstract_inverted_index.environments. | 51 |
| abstract_inverted_index.heterogeneous | 20 |
| abstract_inverted_index.learning-based | 27 |
| abstract_inverted_index.Quantum-Enhanced | 34 |
| abstract_inverted_index.privacy-preserving | 110 |
| abstract_inverted_index.quantum-resilient, | 7 |
| abstract_inverted_index.cross-institutional | 104 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.41955902 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |