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Enabling Privacy-Preserving and Drop-Out Resilient Federated LLM Fine-Tuning for the Low-Altitude UAV Swarm Networks
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Cheng Cheng ,
Liming Nie ,
Junyi Tao
IEEE Transactions on Cognitive Communications and Networking, Volume: 12, Pages: 2919 - 2936
Swansea University Author:
Cheng Cheng
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DOI (Published version): 10.1109/tccn.2025.3620369
Abstract
Advancement of unmanned aerial vehicle (UAV) swarm networks presents transformative opportunities for low-altitude surveillance, disaster response, and distributed sensing, where federated large language models (LLMs) enable collaborative learning while preserving data privacy, enhance swarm-level s...
| Published in: | IEEE Transactions on Cognitive Communications and Networking |
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| ISSN: | 2332-7731 |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2026
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71251 |
| Abstract: |
Advancement of unmanned aerial vehicle (UAV) swarm networks presents transformative opportunities for low-altitude surveillance, disaster response, and distributed sensing, where federated large language models (LLMs) enable collaborative learning while preserving data privacy, enhance swarm-level situational awareness through decentralized knowledge fusion, and support adaptive decision-making across dynamic low-altitude operational environments. However, federated LLM fine-tuning for UAV swarm networks operating in low-altitude settings faces three unresolved security and practical issues: 1) lack of efficient methods to protect parameter security during uplink/downlink transmission under low-altitude communication constraints; 2) absence of effective mechanisms to handle frequent UAV dropouts caused by low-altitude dynamics that may compromise the robustness of federated LLM systems; and 3) constraints in UAVs’ computing, storage and communication resources under typical low-altitude mission profiles. To address these challenges, this paper proposes a Secure and privacy-preserving federated fine-tuning (SPFF) scheme for low-altitude UAV swarms that enables: efficient and privacy-preserving one-to-many distribution of global parameters for downlink federated fine-tuning; secure and efficient uplink local parameter uploading adapted to low-altitude network conditions; and encrypted-parameter-based global model fine-tuning. The scheme also incorporates an efficient supervised key update mechanism to address UAV dropout issues common in low-altitude operations. Moreover, we design a delegable extensional SPFF (DE-SPFF) scheme that employs proxy re-encryption to allow UAVs to delegate tasks to other drones before exiting the federated fine-tuning process in volatile low-altitude environments, while providing public verifiability for re-encryption operations performed by semi-trusted edge nodes. Formal security proofs demonstrate the security of the proposed schemes under low-altitude threat models. Theoretical analysis and experimental results confirm their superiority and practicality for low-altitude UAV swarm applications. |
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| Keywords: |
Autonomous aerial vehicles, Security, Encryption, Federated learning, Data models, Computational efficiency, Vehicle dynamics, Training, Faces, Costs |
| College: |
Faculty of Science and Engineering |
| Funders: |
This work is sponsored by National Natural Science Foundation of China (NSFC) (Grant Number: W2412110), UKRI (Grant Number: EP/W020408/1), Doctoral Training Centre at Swansea University (Grant Number: RS718), and China Postdoctoral Science Foundation (Grant Number: 2024M753597). |
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2919 |
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2936 |