Optimization of trusted wireless sensing models based on deep reinforcement learning for ISAC systems
Abstract This paper investigates using deep reinforcement learning (DRL) methods for optimizing trustworthy federated learning models, with a focus on integrated sensing and communication in practical wireless sensing scenarios. Challenges include computational disparities among edge sensing nodes,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Electronics Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ell2.70080 |
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| _version_ | 1850108688574971904 |
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| author | Hao Zhang Yi Jing Wenhui Xu Ronghui Zhang |
| author_facet | Hao Zhang Yi Jing Wenhui Xu Ronghui Zhang |
| author_sort | Hao Zhang |
| collection | DOAJ |
| description | Abstract This paper investigates using deep reinforcement learning (DRL) methods for optimizing trustworthy federated learning models, with a focus on integrated sensing and communication in practical wireless sensing scenarios. Challenges include computational disparities among edge sensing nodes, network transmission differences, and the non‐independent and identically distributed (non‐IID) nature of local training datasets. As the number of edge sensing nodes increases, the likelihood of encountering untrusted nodes also rises, further limiting the performance of traditional federated learning aggregation algorithms. To address these issues, the paper proposes a DRL‐based strategy aimed at optimizing the node selection process in federated learning environments. This strategy intelligently selects nodes for global aggregation, improving overall model performance and efficiency by addressing computational and communication differences among nodes and the non‐IID nature of data. |
| format | Article |
| id | doaj-art-00f9ee7c8a2c4fe38f7ba3e66cca8aff |
| institution | OA Journals |
| issn | 0013-5194 1350-911X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Electronics Letters |
| spelling | doaj-art-00f9ee7c8a2c4fe38f7ba3e66cca8aff2025-08-20T02:38:18ZengWileyElectronics Letters0013-51941350-911X2024-12-016023n/an/a10.1049/ell2.70080Optimization of trusted wireless sensing models based on deep reinforcement learning for ISAC systemsHao Zhang0Yi Jing1Wenhui Xu2Ronghui Zhang3Hunan First Normal University Changsha ChinaDepartment of Electronic Engineering Tsinghua University Beijing ChinaNational Meteorological Information Center Beijing ChinaSchool of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing ChinaAbstract This paper investigates using deep reinforcement learning (DRL) methods for optimizing trustworthy federated learning models, with a focus on integrated sensing and communication in practical wireless sensing scenarios. Challenges include computational disparities among edge sensing nodes, network transmission differences, and the non‐independent and identically distributed (non‐IID) nature of local training datasets. As the number of edge sensing nodes increases, the likelihood of encountering untrusted nodes also rises, further limiting the performance of traditional federated learning aggregation algorithms. To address these issues, the paper proposes a DRL‐based strategy aimed at optimizing the node selection process in federated learning environments. This strategy intelligently selects nodes for global aggregation, improving overall model performance and efficiency by addressing computational and communication differences among nodes and the non‐IID nature of data.https://doi.org/10.1049/ell2.70080wireless communicationswireless sensor networks |
| spellingShingle | Hao Zhang Yi Jing Wenhui Xu Ronghui Zhang Optimization of trusted wireless sensing models based on deep reinforcement learning for ISAC systems Electronics Letters wireless communications wireless sensor networks |
| title | Optimization of trusted wireless sensing models based on deep reinforcement learning for ISAC systems |
| title_full | Optimization of trusted wireless sensing models based on deep reinforcement learning for ISAC systems |
| title_fullStr | Optimization of trusted wireless sensing models based on deep reinforcement learning for ISAC systems |
| title_full_unstemmed | Optimization of trusted wireless sensing models based on deep reinforcement learning for ISAC systems |
| title_short | Optimization of trusted wireless sensing models based on deep reinforcement learning for ISAC systems |
| title_sort | optimization of trusted wireless sensing models based on deep reinforcement learning for isac systems |
| topic | wireless communications wireless sensor networks |
| url | https://doi.org/10.1049/ell2.70080 |
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