On differential privacy for federated learning in wireless systems with multiple base stations
Abstract In this work, we consider a federated learning model in a wireless system with multiple base stations and inter‐cell interference. We apply a differentially private scheme to transmit information from users to their corresponding base station during the learning phase. We show the convergen...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Communications |
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| Online Access: | https://doi.org/10.1049/cmu2.12722 |
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| _version_ | 1850054163715588096 |
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| author | Nima Tavangaran Mingzhe Chen Zhaohui Yang José Mairton B. Da Silva Jr. H. Vincent Poor |
| author_facet | Nima Tavangaran Mingzhe Chen Zhaohui Yang José Mairton B. Da Silva Jr. H. Vincent Poor |
| author_sort | Nima Tavangaran |
| collection | DOAJ |
| description | Abstract In this work, we consider a federated learning model in a wireless system with multiple base stations and inter‐cell interference. We apply a differentially private scheme to transmit information from users to their corresponding base station during the learning phase. We show the convergence behavior of the learning process by deriving an upper bound on its optimality gap. Furthermore, we define an optimization problem to reduce this upper bound and the total privacy leakage. To find the locally optimal solutions of this problem, we first propose an algorithm that schedules the resource blocks and users. We then extend this scheme to reduce the total privacy leakage by optimizing the differential privacy artificial noise. We apply the solutions of these two procedures as parameters of a federated learning system where each user is equipped with a classifier and communication cells have mostly fewer resource blocks than numbers of users. The simulation results show that our proposed scheduler improves the average accuracy of the predictions compared with a random scheduler. In particular, the results show an improvement of over 6%. Furthermore, its extended version with noise optimizer significantly reduces the amount of privacy leakage. |
| format | Article |
| id | doaj-art-b95d6b1304a04bee90ef575c5dc35012 |
| institution | DOAJ |
| issn | 1751-8628 1751-8636 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj-art-b95d6b1304a04bee90ef575c5dc350122025-08-20T02:52:20ZengWileyIET Communications1751-86281751-86362024-12-0118201853186710.1049/cmu2.12722On differential privacy for federated learning in wireless systems with multiple base stationsNima Tavangaran0Mingzhe Chen1Zhaohui Yang2José Mairton B. Da Silva Jr.3H. Vincent Poor4Department of Electrical and Computer Engineering Princeton University Princeton New Jersey USADepartment of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables Florida USACollege of Information Science and Electronic Engineering Zhejiang University Hangzhou ChinaDepartment of Information Technology Uppsala University Uppsala SwedenDepartment of Electrical and Computer Engineering Princeton University Princeton New Jersey USAAbstract In this work, we consider a federated learning model in a wireless system with multiple base stations and inter‐cell interference. We apply a differentially private scheme to transmit information from users to their corresponding base station during the learning phase. We show the convergence behavior of the learning process by deriving an upper bound on its optimality gap. Furthermore, we define an optimization problem to reduce this upper bound and the total privacy leakage. To find the locally optimal solutions of this problem, we first propose an algorithm that schedules the resource blocks and users. We then extend this scheme to reduce the total privacy leakage by optimizing the differential privacy artificial noise. We apply the solutions of these two procedures as parameters of a federated learning system where each user is equipped with a classifier and communication cells have mostly fewer resource blocks than numbers of users. The simulation results show that our proposed scheduler improves the average accuracy of the predictions compared with a random scheduler. In particular, the results show an improvement of over 6%. Furthermore, its extended version with noise optimizer significantly reduces the amount of privacy leakage.https://doi.org/10.1049/cmu2.127226Gdata privacyfederated learningoptimizationschedulingwireless channels |
| spellingShingle | Nima Tavangaran Mingzhe Chen Zhaohui Yang José Mairton B. Da Silva Jr. H. Vincent Poor On differential privacy for federated learning in wireless systems with multiple base stations IET Communications 6G data privacy federated learning optimization scheduling wireless channels |
| title | On differential privacy for federated learning in wireless systems with multiple base stations |
| title_full | On differential privacy for federated learning in wireless systems with multiple base stations |
| title_fullStr | On differential privacy for federated learning in wireless systems with multiple base stations |
| title_full_unstemmed | On differential privacy for federated learning in wireless systems with multiple base stations |
| title_short | On differential privacy for federated learning in wireless systems with multiple base stations |
| title_sort | on differential privacy for federated learning in wireless systems with multiple base stations |
| topic | 6G data privacy federated learning optimization scheduling wireless channels |
| url | https://doi.org/10.1049/cmu2.12722 |
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