Blockchain-based federal learning program for drone safety

Jingyuan Jing; Yanbo Yang; Mingchao Li; Baoshan Li; Jiawei Zhang; Jianfeng Ma

doi:10.1117/12.3031895

6 June 2024 Blockchain-based federal learning program for drone safety

Jingyuan Jing, Yanbo Yang, Mingchao Li, Baoshan Li, Jiawei Zhang, Jianfeng Ma

Proceedings Volume 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024); 131751P (2024) https://doi.org/10.1117/12.3031895
Event: 4th International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 2024, Sanya, China

Abstract

In order to study secure federated learning for resource-constrained devices such as drones to protect user privacy and data security in drone networks, a blockchain-based secure federated learning scheme for drones is proposed. Currently, researchers focus on transferring models for federated learning after local training using drones, but in reality, drones will be limited in accomplishing local training due to their own resource and arithmetic issues. In this paper, the scheme offloads the training task of the UAV to the local server, and the UAV is only responsible for performing model aggregation and delivery. At the same time, a new consensus algorithm PoE (Proof-of-Energy) is proposed to model the energy and evaluate the arithmetic power of drones, which assigns roles to each drone node within the blockchain network and ensures that the drones effectively participate in the federated learning process. Due to the open and transparent nature of the blockchain, ring signatures are used to replace the traditional signatures in order to protect the private information such as the behavior and identity of each node and the content of block transactions. The experimental results show that the proposed model can ensure that UAVs effectively participate in federated learning. In addition, when there is a poisoning sample to disrupt the training process, the accuracy of the global model can be effectively ensured compared to the traditional scheme.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jingyuan Jing, Yanbo Yang, Mingchao Li, Baoshan Li, Jiawei Zhang, and Jianfeng Ma "Blockchain-based federal learning program for drone safety", Proc. SPIE 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 131751P (6 June 2024); https://doi.org/10.1117/12.3031895

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KEYWORDS

Machine learning

Education and training

Blockchain

Unmanned aerial vehicles

Data modeling

Instrument modeling

Data privacy

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