Learning in the Air: Secure Federated Learning for UAV-Assisted Crowdsensing
Abstract
Unmanned aerial vehicles (UAVs) combined with artificial intelligence (AI) have opened a revolutionized way for mobile crowdsensing (MCS). Conventional AI models, built on aggregation of UAVs' sensing data (typically contain private and sensitive user information), may arise severe privacy and data misuse concerns. Federated learning, as a promising distributed AI paradigm, has opened up possibilities for UAVs to collaboratively train a shared global model without revealing their local sensing data. However, there still exist potential security and privacy threats for UAV-assisted crowdsensing with federated learning due to vulnerability of central curator, unreliable contribution recording, and low-quality shared local models. In this paper, we propose SFAC, a secure federated learning framework for UAV-assisted MCS. Specifically, we first introduce a blockchain-based collaborative learning architecture for UAVs to securely exchange local model updates and verify contributions without the central curator. Then, by applying local differential privacy, we design a privacy-preserving algorithm to protect UAVs' privacy of updated local models with desirable learning accuracy. Furthermore, a two-tier reinforcement learning-based incentive mechanism is exploited to promote UAVs' high-quality model sharing when explicit knowledge of network parameters are not available in practice. Extensive simulations are conducted, and the results demonstrate that the proposed SFAC can effectively improve utilities for UAVs, promote high-quality model sharing, and ensure privacy protection in federated learning, compared with existing schemes.
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In the UAV crowdsensing FLchain scheme of [65], blockchain is employed to build a decentralized learning network by interconnecting UAVs with data task publishers through an immutable ledger, which ensures that data training and model exchange among UAVs are monitored and traced for attack detection and data modification prevention.
“Blockchain is employed to build a decentralized learning network by interconnecting UAVs with data task publishers through an immutable ledger, which ensures that data training and model exchange among UAVs are monitored and traced for attack detection and data modification prevention.”
From Federated learning meets blockchain in edge computing: Opportunities and challenges (2021) · cites this paper as [65]
An FLchain scheme is studied in [65] to construct a mobile crowdsensing system based on Unmanned Aerial Vehicles (UAVs).
“For example, an FLchain scheme is studied in [65] to construct a mobile crowdsensing system based on Unmanned Aerial Vehicles (UAVs), as indicated in Fig. 6.”
From Federated learning meets blockchain in edge computing: Opportunities and challenges (2021) · cites this paper as [65]
Evaluation results confirm a high utility of UAVs and low aggregation error while reducing convergence latency for the differential privacy and incentive mechanism approach in [65].
“Evaluation results confirm a high utility of UAVs and low aggregation error while reducing convergence latency.”
From Federated learning meets blockchain in edge computing: Opportunities and challenges (2021) · cites this paper as [65]
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Cites
DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive
Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W. (2019) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2019.2952332 · Cited by 610 · see citing works
A Learning-Based Incentive Mechanism for Federated Learning
Zhan, Y., Li, P., Qu, Z., Zeng, D., & Guo, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2967772 · Cited by 602 · see citing works
Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT
Mills, J., Hu, J., & Min, G. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2956615 · Cited by 436 · see citing works
Robust Mobile Crowd Sensing: When Deep Learning Meets Edge Computing
Zhou, Z., Liao, H., Gu, B., Huq, K. M. S., Mumtaz, S., & Rodriguez, J. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700442 · Cited by 260 · see citing works
BSIS: Blockchain-Based Secure Incentive Scheme for Energy Delivery in Vehicular Energy Network
Wang, Y., Su, Z., & Zhang, N. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2908497 · Cited by 218 · see citing works
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Federated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3075439 · Cited by 1,503 · see citing works
“[100] Mobile crowdsensing DFL RL UAVs Task publishers A blockchain-based FL scheme for mobile sensing in UAV networks.”
BackgroundMethodInfluentialFederated learning meets blockchain in edge computing: Opportunities and challenges
Nguyen, D. C., Ding, M., Pham, Q.-V., Pathirana, P. N., Le, L. B., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3072611 · Cited by 661 · see citing works
Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
Martínez Beltrán, E. T., Pérez, M. Q., Sánchez, P. M. S., Bernal, S. L., Bovet, G., Pérez, M. G., Pérez, G. M., & Celdrán, A. H. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3315746 · Cited by 625 · see citing works
“The increase in the number of papers using DFL in different scenarios: healthcare [2], [21], Industry 4.0 [17], mobile services [18], military [19], and vehicles [22] motivate the need to identify the fundamental elements of DFL and review the work done to date.”
BackgroundMethodInfluentialFederated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey
Ali, M., Naeem, F., Tariq, M., & Kaddoum, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3181823 · Cited by 366 · see citing works
Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342 · see citing works
Blockchain-empowered Federated Learning: Challenges, Solutions, and Future Directions
Zhu, J., Cao, J., Saxena, D., Jiang, S., & Ferradi, H. (2023) · ACM Computing Surveys · doi:10.1145/3570953 · Cited by 309 · see citing works
Drones’ Edge Intelligence Over Smart Environments in B5G: Blockchain and Federated Learning Synergy
Alsamhi, S. H., Almalki, F. A., Afghah, F., Hawbani, A., Shvetsov, A. V., Lee, B., & Song, H. (2022) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2021.3132561 · Cited by 139 · see citing works
M., P., Bourouis, S., Ahmed, A. N., M., V., K.G.S., V., Alhakami, W., & Hamdi, M. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.05.014 · Cited by 108 · see citing works
Zhan, S., Huang, L., Luo, G., Zheng, S., Gao, Z., & Chao, H.-C. (2025) · Electronics · doi:10.3390/electronics14132512 · Cited by 82 · see citing works
Secure and Personalized Edge Computing Services in 6G Heterogeneous Vehicular Networks
Hui, Y., Cheng, N., Su, Z., Huang, Y., Zhao, P., Luan, T. H., & Li, C. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3065970 · Cited by 76 · see citing works
Self-citation
- Authors
- Wang, Y., Su, Z., Zhang, N., & Benslimane, A.
- Affiliations
- Xi'an Jiaotong University (CN)
- Shanghai University (CN)
- University of Windsor (CA)
- Université d'Avignon et des Pays de Vaucluse (FR)
- Venue
- IEEE Transactions on Network Science and Engineering
- Published
- 2021-04-01
- Volume
- 8
- Issue
- 2
- Pages
- 1055-1069
- ISSN
- 2327-4697 (ISSN-L)
- 2334-329X
- OpenAlex
- W3048295601
- Cited by
- 323
- References
- 5
- Citations by year
- 2026: 36 · 2025: 62 · 2024: 70 · 2023: 72 · 2022: 44 · 2021: 37 · 2020: 2
- FWCI
- 15.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Network Science and Engineering (publishedVersion)
- HAL (submittedVersion)
- Funders
- National Natural Science Foundation of China (U1808207, 91746114)
- Science and Technology Commission of Shanghai Municipality (18510761000)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Keywords
- secure federated learning, mobile crowdsensing, differential privacy, privacy preservation, incentive mechanisms, reinforcement learning
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