Signal Modulation Recognition Method Based on Differential Privacy Federated Learning

Shi, Jie; Qi, Lin; Li, Kuixian; Lin, Yun

doi:10.1155/2021/2537546

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…As these studies point out, CentAMR poses a significant privacy risk due to the extensive data transfer to the central server for model training. 2,5,26,27 However, it is worth noting that this challenge may not be as significant in wireless networks where signals are accessible. A study by Shi et al 27 explores federated learning-based AMR with differential privacy, specifically concentrating on data privacy in AMR tasks.…”

Section: Related Workmentioning

confidence: 99%

“…2,5,26,27 However, it is worth noting that this challenge may not be as significant in wireless networks where signals are accessible. A study by Shi et al 27 explores federated learning-based AMR with differential privacy, specifically concentrating on data privacy in AMR tasks. They show that their achieved accuracy is acceptable compared to the centralized model, while ensuring data security.…”

Section: Related Workmentioning

confidence: 99%

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Distributed learning for automatic modulation recognition in bandwidth-limited networks

Rashvand,

Witham,

Maldonado

et al. 2024

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

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Automatic Modulation Recognition (AMR) is critical in identifying various modulation types in wireless communication systems. Recent advancements in deep learning have facilitated the integration of algorithms into AMR techniques. However, this integration typically follows a centralized approach that necessitates collecting and processing all training data on high-powered computing devices, which may prove impractical for bandwidth-limited wireless networks.In response to this challenge, this study introduces two methods for distributed learning-based AMR on the collaboration of multiple receivers to perform AMR tasks. The TeMuRAMRD 2023 dataset is employed to support this investigation, uniquely suited for multi-receiver AMR tasks. Within this distributed sensing environment, multiple receivers collaborate in identifying modulation types from the same RF signal, each possessing a partial perspective of the overall environment.Experimental results demonstrate that the centralized-based AMR, with six receivers, attains an impressive accuracy rate of 91%, while individual receivers exhibit a notably lower accuracy, at around 41%. Nonetheless, the two proposed decentralized learning-based AMR methods exhibit noteworthy enhancements. Based on consensus voting among six receivers, the initial method achieves a marginally lower accuracy. It achieves this while substantially reducing the bandwidth demands to a 1/256th of the centralized model. With the second distributed method, each receiver shares its feature map, subsequently aggregated by a central node. This approach also accompanies a substantial bandwidth reduction of 1/8 compared to the centralized approach. These findings highlight the capacity of distributed AMR to significantly enhance accuracy while effectively addressing the constraints of bandwidth-limited wireless networks.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Distributed learning for automatic modulation recognition in bandwidth-limited networks

Rashvand,

Witham,

Maldonado

et al. 2024

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

View full text Add to dashboard Cite

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“…A CNN-based federated learning approach was proposed in ref. [100], enabling differential privacy for modulation recognition in order to assure the privacy and security of transmitted data.…”

Section: Artificial Intelligencementioning

confidence: 99%

6G—Enabling the New Smart City: A Survey

Murroni,

Anedda,

Fadda

et al. 2023

Sensors

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Smart cities and 6G are technological areas that have the potential to transform the way we live and work in the years to come. Until this transformation comes into place, there is the need, underlined by research and market studies, for a critical reassessment of the entire wireless communication sector for smart cities, which should include the IoT infrastructure, economic factors that could improve their adoption rate, and strategies that enable smart city operations. Therefore, from a technical point of view, a series of stringent issues, such as interoperability, data privacy, security, the digital divide, and implementation issues have to be addressed. Notably, to concentrate the scrutiny on smart cities and the forthcoming influence of 6G, the groundwork laid by the current 5G, with its multifaceted role and inherent limitations within the domain of smart cities, is embraced as a foundational standpoint. This examination culminates in a panoramic exposition, extending beyond the mere delineation of the 6G standard toward the unveiling of the extensive gamut of potential applications that this emergent standard promises to introduce to the smart cities arena. This paper provides an update on the SC ecosystem around the novel paradigm of 6G, aggregating a series of enabling technologies accompanied by the descriptions of their roles and specific employment schemes.

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“…The authors found that DL algorithms perform modulation recognition more accurately compared to ML algorithms such as the Support Vector Machine (SVM). To ensure the privacy and security of the transmitted data, Shi et al [ 46 ] proposed a CNN-based federated learning approach with differential privacy for modulation recognition.…”

Section: Artificial Intelligence (Ai)-enabled 6g Networkmentioning

confidence: 99%

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Ismail

Buyya

2022

Sensors

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The recent upsurge of smart cities’ applications and their building blocks in terms of the Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data analytics, blockchain, and edge-cloud computing has urged the design of the upcoming 6G network generation, due to their stringent requirements in terms of the quality of services (QoS), availability, and dependability to satisfy a Service-Level-Agreement (SLA) for the end users. Industries and academia have started to design 6G networks and propose the use of AI in its protocols and operations. Published papers on the topic discuss either the requirements of applications via a top-down approach or the network requirements in terms of agility, performance, and energy saving using a down-top perspective. In contrast, this paper adopts a holistic outlook, considering the applications, the middleware, the underlying technologies, and the 6G network systems towards an intelligent and integrated computing, communication, coordination, and decision-making ecosystem. In particular, we discuss the temporal evolution of the wireless network generations’ development to capture the applications, middleware, and technological requirements that led to the development of the network generation systems from 1G to AI-enabled 6G and its employed self-learning models. We provide a taxonomy of the technology-enabled smart city applications’ systems and present insights into those systems for the realization of a trustworthy and efficient smart city ecosystem. We propose future research directions in 6G networks for smart city applications.

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Signal Modulation Recognition Method Based on Differential Privacy Federated Learning

Cited by 10 publications

References 24 publications

Distributed learning for automatic modulation recognition in bandwidth-limited networks

Distributed learning for automatic modulation recognition in bandwidth-limited networks

6G—Enabling the New Smart City: A Survey

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

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