FedProLs: federated learning for IoT perception data prediction

Zeng, Qingtian; Lv, Zhenzhen; Li, Chao; Shi, Yongkui; Lin, Zedong; Liu, Cong; Song, Ge

doi:10.1007/s10489-022-03578-1

Cited by 12 publications

(11 citation statements)

References 25 publications

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“…The algorithm is based on the stochastic gradient descent algorithm improvement, which initializes the weights and distributes them to each device or computing node for training through a central server. This method optimizes model training to improve the efficiency and accuracy of data processing while also protecting privacy [17][18][19] . After each iteration, a certain proportion of participants will be selected from the iteration results for optimization.…”

Section: Data Imbalance Processing On the Grounds Of Fedavgmentioning

confidence: 99%

Privacy Protection Based on Federated Learning

Liu,

Eric B. Blancaflor,

Fang

et al. 2024

JAIT

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With the development of artificial intelligence technology, more and more fields will collect relevant user data, and provide users with a better experience through data analysis. But there are also risks involved in the process of data collection, namely how to protect personal privacy data. To address this issue, this study combined differential privacy, convolutional neural networks, and federated averaging algorithms to construct a privacy protection model. The study first utilized the federated average algorithm to handle data imbalance, ensuring that each analyzed data is in a balanced state. Then, based on of data balancing, a new algorithm model was constructed using differential privacy and convolutional neural networks. Finally, it utilized a number of public datasets to verify the role of the model in privacy protection. The results showed that the model can achieve recognition accuracy of 97.27% and 93.15%, respectively , for data under the influence of privacy budget and relaxation factor. Meanwhile, the classification accuracy of the model for data can reached 95.31%, with a regression error of 9.03%. When the local iteration number of the device was 30, the testing accuracy can reached 95.28%. This indicates that methods on the grounds of federated averaging algorithm and differential privacy can maintain the accuracy of the model while protecting user privacy. The application research of models has strong practical significance.

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Section: Data Imbalance Processing On the Grounds Of Fedavgmentioning

confidence: 99%

Privacy Protection Based on Federated Learning

Liu,

Eric B. Blancaflor,

Fang

et al. 2024

JAIT

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“…A federated learning approach is promising for AI based on -WSN. It allows models to be trained across multiple edge devices or servers while keeping data localized [588]. In agriculture, where data privacy is crucial, this technique enables collaborative model training without centralized data storage [589].…”

Section: ) Federated Learning (Fl)mentioning

confidence: 99%

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

Mowla,

Shah

et al. 2023

IEEE Access

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The increasing food scarcity necessitates sustainable agriculture achieved through automation to meet the growing demand. Integrating the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) is crucial in enhancing food production across various agricultural domains, encompassing irrigation, soil moisture monitoring, fertilizer optimization and control, early-stage pest and crop disease management, and energy conservation. Wireless application protocols such as ZigBee, WiFi, SigFox, and LoRaWAN are commonly employed to collect real-time data for monitoring purposes. Embracing advanced technology is imperative to ensure efficient annual production. Therefore, this study emphasizes a comprehensive, futureoriented approach, delving into IoT-WSNs, wireless network protocols, and their applications in agriculture since 2019. It thoroughly discusses the overview of IoT and WSNs, encompassing their architectures and summarization of network protocols. Furthermore, the study addresses recent issues and challenges related to IoT-WSNs and proposes mitigation strategies. It provides clear recommendations for the future, emphasizing the integration of advanced technology aiming to contribute to the future development of smart agriculture systems. INDEX TERMSInternet of things, wireless sensor networks, wireless network protocols, smart agriculture applications.

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“…Another research to prevent security is the distributed/federated learning approach. Zeng et al presented a framework for federated learning to ensure IoT data privacy and security [33]. Qi et al discussed that the method is not convenient for industrial IoT platforms since the data are generated at a high rate, and collecting data among clusters may create a waiting queue [12].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach With GAN and DP for Privacy Preservation of IIoT Data

Hindistan

Yetkin

2023

IEEE Access

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There are emerging trends to use the Industrial Internet of Things (IIoT) in manufacturing and related industries. Machine Learning (ML) techniques are widely used to interpret the collected IoT data for improving the company's operational excellence and predictive maintenance. In general, ML applications require high computational resource allocation and expertise. Manufacturing companies usually transfer their IIoT data to an ML-enabled third party or a cloud system. Although the transmission process uses encryption, ML applications still need decrypted data to perform ML tasks efficiently. Therefore, the third parties may have unacceptable access rights during the data processing to the content of IIoT data that contains a portrait of the production process. IIoT data may include hidden sensitive features, creating an information leakage for the companies. All these concerns prevent companies from sharing their IIoT data with third parties. This paper proposes a novel method based on the hybrid usage of Generative Adversarial Networks (GAN) and Differential Privacy (DP) to preserve sensitive data in IIoT operations. We aim to sustain IIoT data privacy with minimal accuracy loss without adding high additional computational costs to the overall data processing scheme. We demonstrate the efficiency of our approach with publicly available data sets and a realistic IIoT data set collected from a confectionery production process.

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FedProLs: federated learning for IoT perception data prediction

Cited by 12 publications

References 25 publications

Privacy Protection Based on Federated Learning

Privacy Protection Based on Federated Learning

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

A Hybrid Approach With GAN and DP for Privacy Preservation of IIoT Data

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