无人机技术智能环境中基于区块链的联邦学习方法

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Mukkoti Maruthi Venkata Chalapathi, K. Sreenivasulu, R. Jeya, Muhammad Faheem, R. Madana Mohana, Arfat Ahmad Khan, Kadiyala Ramana
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引用次数: 0

摘要

使用区块链技术将高安全性交易存储在区块链中。使用区块链技术可以解决安全和隐私问题。联邦学习是一种通过确保智能环境中物联网(IoT)设备和用户的数据隐私和安全性来提高数据挖掘准确性和精度的范例。该模型包含了处理有限训练数据和避免特定模型的算法。无人机确实被研究并积极用于紧急情况,以及灾难性和高伤亡情况。医疗无人机网络的治理、安全、飞行环境、安全和隐私、授权、保密以及创建、维护和运行的具体细节,现在是扩大其在急诊医学和紧急医疗服务(EMS)中的使用的障碍。在本文中,我们提出了更有效的FL来保护无人机的数据隐私,其中包括对无人机进行本地和全局参数更新以及交换有关雾节点的训练参数,而不是将无人机原始数据发送到云端。即便如此,在训练过程中上传的窃听和分析参数仍可能为地面窃听者提供无人机隐私和操作信息。具体而言,在这项工作中,我们研究了如何优化电源管理策略,以优化FL安全成本所需的所有参数,同时受无人机容量的电池使用情况和服务质量(QoS)的必要性(即所需的培训时间)的约束。大量的仿真结果表明,与现有方案相比,所提出的安全联邦功率控制(SFPC)可以有效地提高无人机的效用,促进高质量的模型共享,并确保联邦学习中的隐私保护。©2025作者。电气与电子工程学报,日本电气工程师学会和Wiley期刊公司出版。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Blockchain-Based Federated Learning Methodologies in Smart Environments for Drone Technology

Blockchain-Based Federated Learning Methodologies in Smart Environments for Drone Technology

High-security transactions are stored in a chain of blocks using blockchain technology. Security and privacy concerns may be addressed by using blockchain technology. Federated learning is a paradigm for increasing data mining accuracy and precision by ensuring data privacy and security for both internet of things (IoT) devices and users in smart environments. Algorithms for dealing with limited training data and avoiding a particular model are included in the proposed model. Drones are indeed being researched and proactively employed in emergency situations, as well as catastrophic and high-casualty situations. Governance, security, flying circumstances, security and privacy, authorization, confidentiality, and specifics around the creation, maintenance, and operation of a medical drone network are now obstacles to extending their usage in emergency medicine and emergency medical service (EMS). In this paper, we present the more effective FL to protect the data privacy of drones, which involves doing local and global parameter updates for drones and exchanging training parameters concerning fog nodes, rather than sending drone raw data to the cloud. Even so, eavesdropping and analyzing parameters that are uploaded during the training procedure might still provide ground eavesdroppers with information on drone privacy and operations. Specifically, in this work, we examine how to optimize the power management strategies to optimize all the required parameters of FL security cost while being bound by battery usage of drone capacity and the necessity for quality of service (QoS) (i.e., required training time). Extensive simulations were conducted, and the results demonstrate that the proposed Secure Federated Power Control (SFPC) can effectively improve utilities for drones, promote high-quality model sharing, and ensure privacy protection in federated learning, compared with existing schemes. © 2025 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

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来源期刊
IEEJ Transactions on Electrical and Electronic Engineering
IEEJ Transactions on Electrical and Electronic Engineering 工程技术-工程:电子与电气
CiteScore
2.70
自引率
10.00%
发文量
199
审稿时长
4.3 months
期刊介绍: IEEJ Transactions on Electrical and Electronic Engineering (hereinafter called TEEE ) publishes 6 times per year as an official journal of the Institute of Electrical Engineers of Japan (hereinafter "IEEJ"). This peer-reviewed journal contains original research papers and review articles on the most important and latest technological advances in core areas of Electrical and Electronic Engineering and in related disciplines. The journal also publishes short communications reporting on the results of the latest research activities TEEE ) aims to provide a new forum for IEEJ members in Japan as well as fellow researchers in Electrical and Electronic Engineering from around the world to exchange ideas and research findings.
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