On differential privacy for federated learning in wireless systems with multiple base stations
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 behavi...
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| Veröffentlicht in: | IET communications Jg. 18; H. 20; S. 1853 - 1867 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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Wiley
01.12.2024
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| ISSN: | 1751-8628, 1751-8636, 1751-8636 |
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| 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.
Here, 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 and analyse the performance of such a system. |
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| AbstractList | 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. 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. Here, 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 and analyse the performance of such a system. 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. |
| Author | Tavangaran, Nima Yang, Zhaohui Da Silva, José Mairton B. Poor, H. Vincent Chen, Mingzhe |
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| References | 2021; 9 2021; 8 2021; 20 2020; 20 2012 2021; 28 2019; 37 1995; 33 2022; 23 1998 2020; 37 2020; 15 2006 2022; 26 2020; 585 2016; 17 2012; 34 2020; 19 2016; 11 2021; 10 2018; 5 2020; 2 2023 2022 2022; 40 2020 2019; 68 2021; 39 2022; 9 2019 2007; 9 2007; 6 2022; 15 2017 2016 2022; 30 2015 2013 2014; 9 2003; 87 2022; 18 e_1_2_12_4_1 e_1_2_12_6_1 e_1_2_12_19_1 e_1_2_12_2_1 e_1_2_12_17_1 e_1_2_12_38_1 Diamond S. (e_1_2_12_36_1) 2016; 17 e_1_2_12_20_1 e_1_2_12_41_1 e_1_2_12_22_1 e_1_2_12_43_1 e_1_2_12_24_1 e_1_2_12_26_1 Nesterov Y. (e_1_2_12_45_1) 2003 e_1_2_12_47_1 Bazaraa M.S. (e_1_2_12_51_1) 2013 Dahlman E. (e_1_2_12_48_1) 2020 e_1_2_12_28_1 e_1_2_12_49_1 e_1_2_12_31_1 e_1_2_12_52_1 e_1_2_12_33_1 e_1_2_12_35_1 e_1_2_12_37_1 e_1_2_12_14_1 e_1_2_12_12_1 e_1_2_12_8_1 e_1_2_12_10_1 e_1_2_12_50_1 e_1_2_12_3_1 e_1_2_12_5_1 e_1_2_12_18_1 e_1_2_12_16_1 e_1_2_12_39_1 e_1_2_12_42_1 e_1_2_12_21_1 e_1_2_12_44_1 e_1_2_12_23_1 e_1_2_12_46_1 e_1_2_12_25_1 e_1_2_12_40_1 e_1_2_12_27_1 e_1_2_12_29_1 e_1_2_12_30_1 e_1_2_12_53_1 e_1_2_12_32_1 e_1_2_12_34_1 e_1_2_12_15_1 e_1_2_12_13_1 e_1_2_12_11_1 e_1_2_12_7_1 e_1_2_12_9_1 |
| References_xml | – volume: 9 start-page: 16592 issue: 17 year: 2022 end-page: 16605 article-title: Federated learning over wireless IoT networks with optimized communication and resources publication-title: IEEE Internet Things J. – volume: 39 start-page: 3821 issue: 12 year: 2021 end-page: 3835 article-title: Privacy amplification for federated learning via user sampling and wireless aggregation publication-title: IEEE J. Sel. Areas Commun. – volume: 23 start-page: 812 issue: 1 year: 2022 end-page: 822 article-title: Scalable and low‐latency federated learning with cooperative mobile edge networking publication-title: IEEE Trans. Mob. Comput. – start-page: 265 year: 2006 end-page: 284 article-title: Calibrating noise to sensitivity in private data analysis – volume: 17 start-page: 1 issue: 83 year: 2016 end-page: 5 article-title: CVXPY: A Python‐embedded modeling language for convex optimization publication-title: J. Mach. Learn. Res. – volume: 6 start-page: 2807 issue: 8 year: 2007 end-page: 2812 article-title: A resource allocator for the uplink of multi‐cell OFDMA systems publication-title: IEEE Trans. Wireless Commun. – volume: 37 start-page: 50 issue: 3 year: 2020 end-page: 60 article-title: Federated learning: Challenges, methods, and future directions publication-title: IEEE Signal Process. Mag. – volume: 9 start-page: 90 issue: 3 year: 2007 end-page: 95 article-title: Matplotlib: A 2D graphics environment publication-title: IEEE Comput. Sci. Eng. – volume: 10 start-page: 1434 issue: 7 year: 2021 end-page: 1438 article-title: Online client scheduling for fast federated learning publication-title: IEEE Wireless Commun. Lett. – volume: 40 start-page: 290 issue: 1 year: 2022 end-page: 307 article-title: Low‐latency federated learning over wireless channels with differential privacy publication-title: IEEE J. Sel. Areas Commun. – volume: 20 start-page: 1935 issue: 3 year: 2021 end-page: 1949 article-title: Energy efficient federated learning over wireless communication networks publication-title: IEEE Trans. Wireless Commun. – volume: 87 year: 2003 – year: 1998 – start-page: 635 year: 2016 end-page: 658 article-title: Concentrated differential privacy: Simplifications, extensions, and lower bounds – year: 2020 article-title: Concentrated differentially private and utility preserving federated learning – volume: 585 start-page: 357 issue: 7825 year: 2020 end-page: 362 article-title: Array programming with NumPy publication-title: Nature – volume: 34 start-page: A1380 issue: 3 year: 2012 end-page: A1405 article-title: Hybrid deterministic‐stochastic methods for data fitting publication-title: SIAM J. Sci. Comput. – year: 2016 article-title: Federated optimization: Distributed machine learning for on‐device intelligence – year: 2022 – volume: 15 start-page: 290 issue: 4 year: 2022 end-page: 399 article-title: Wireless for machine learning: A survey publication-title: Found. Trends Signal Process. – volume: 28 start-page: 192 issue: 5 year: 2021 end-page: 198 article-title: Dispersed federated learning: Vision, taxonomy, and future directions publication-title: IEEE Wireless Commun. – start-page: 3071 year: 2013 end-page: 3076 article-title: ECOS: An SOCP solver for embedded systems – volume: 26 start-page: 1489 issue: 7 year: 2022 end-page: 1493 article-title: Privacy‐preserving federated edge learning: Modelling and optimization publication-title: IEEE Commun. Lett. – volume: 18 start-page: 6273 issue: 9 year: 2022 end-page: 6282 article-title: Decentralized wireless federated learning with differential privacy publication-title: IEEE Trans. Industr. Inform. – volume: 9 start-page: 572 issue: 1 year: 2022 end-page: 588 article-title: Edge‐assisted democratized learning toward federated analytics publication-title: IEEE Internet Things J. – year: 2015 – volume: 68 start-page: 1146 issue: 2 year: 2019 end-page: 1159 article-title: Distributed federated learning for ultra‐reliable low‐latency vehicular communications publication-title: IEEE Trans. Commun. – volume: 19 start-page: 3546 issue: 5 year: 2020 end-page: 3557 article-title: Federated learning over wireless fading channels publication-title: IEEE Trans. Wireless Commun. – start-page: 1 year: 2022 end-page: 12 article-title: A dispersed federated learning framework for 6G‐enabled autonomous driving cars publication-title: IEEE Trans. Netw. Sci. Eng. – volume: 8 start-page: 10639 issue: 13 year: 2021 end-page: 10651 article-title: Incentivizing differentially private federated learning: A multi‐dimensional contract approach publication-title: IEEE Internet Things J. – volume: 30 start-page: 1569 issue: 4 year: 2022 end-page: 1584 article-title: Multi‐stage hybrid federated learning over large‐scale D2D‐enabled fog networks publication-title: IEEE/ACM Trans. Netw. – volume: 9 start-page: 11085 issue: 13 year: 2022 end-page: 11097 article-title: THF: 3‐way hierarchical framework for efficient client selection and resource management in federated learning publication-title: IEEE Internet Things J. – start-page: 1 year: 2019 end-page: 15 article-title: Towards federated learning at scale: System design – volume: 11 start-page: 2648 issue: 12 year: 2016 end-page: 2663 article-title: Reconstruction attacks against mobile‐based continuous authentication systems in the cloud publication-title: IEEE Trans. Inf. Forensics Secur. – start-page: 1387 year: 2019 end-page: 1395 article-title: Federated learning over wireless networks: Optimization model design and analysis – volume: 33 start-page: 42 issue: 1 year: 1995 end-page: 49 article-title: Propagation measurements and models for wireless communications channels publication-title: IEEE Commun. Mag. – volume: 37 start-page: 1205 issue: 6 year: 2019 end-page: 1221 article-title: Adaptive federated learning in resource constrained edge computing systems publication-title: IEEE J. Sel. Areas Commun. – volume: 9 start-page: 211 issue: 3‐4 year: 2014 end-page: 407 article-title: The algorithmic foundations of differential privacy publication-title: Found. Trends Theo. Comput. Sci. – start-page: 1 year: 2020 end-page: 6 article-title: Energy‐efficient radio resource allocation for federated edge learning – volume: 9 start-page: 92 issue: 1 year: 2021 end-page: 103 article-title: Federated learning over energy harvesting wireless networks publication-title: IEEE Internet Things J. – volume: 5 start-page: 42 issue: 1 year: 2018 end-page: 60 article-title: A rewriting system for convex optimization problems publication-title: J. Contr. Dec. – start-page: 308 year: 2016 end-page: 318 article-title: Deep learning with differential privacy – volume: 40 start-page: 2361 issue: 8 year: 2022 end-page: 2377 article-title: Interference management for over‐the‐air federated learning in multi‐cell wireless networks publication-title: IEEE J. Sel. Areas Commun. – year: 2012 – volume: 15 start-page: 3454 year: 2020 end-page: 3469 article-title: Federated learning with differential privacy: Algorithms and performance analysis publication-title: IEEE Trans. Inf. Forensics Secur. – volume: 2 start-page: 429 year: 2020 end-page: 450 article-title: Federated optimization in heterogeneous networks – start-page: 1273 year: 2017 end-page: 1282 article-title: Communication‐efficient learning of deep networks from decentralized data – volume: 20 start-page: 269 issue: 1 year: 2020 end-page: 283 article-title: A joint learning and communications framework for federated learning over wireless networks publication-title: IEEE Trans. Wireless Commun. – year: 2020 – volume: 39 start-page: 3805 issue: 12 year: 2021 end-page: 3820 article-title: Pain‐FL: Personalized privacy‐preserving incentive for federated learning publication-title: IEEE J. Sel. Areas Commun. – year: 2023 – year: 2013 – ident: e_1_2_12_52_1 – volume-title: Introductory Lectures on Convex Optimization: A Basic Course year: 2003 ident: e_1_2_12_45_1 – ident: e_1_2_12_20_1 doi: 10.1109/MWC.011.2100003 – ident: e_1_2_12_40_1 doi: 10.23919/ECC.2013.6669541 – ident: e_1_2_12_14_1 doi: 10.1109/JIOT.2022.3151193 – ident: e_1_2_12_19_1 doi: 10.1109/TNET.2022.3143495 – ident: e_1_2_12_46_1 doi: 10.1145/2976749.2978318 – ident: e_1_2_12_9_1 doi: 10.1109/TWC.2020.3024629 – ident: e_1_2_12_26_1 doi: 10.1007/11681878_14 – ident: e_1_2_12_5_1 – ident: e_1_2_12_8_1 doi: 10.1109/TCOMM.2019.2956472 – ident: e_1_2_12_3_1 doi: 10.1561/2000000114 – ident: e_1_2_12_25_1 doi: 10.1109/TIFS.2016.2594132 – ident: e_1_2_12_30_1 doi: 10.1109/JSAC.2021.3118354 – ident: e_1_2_12_13_1 doi: 10.1109/JSAC.2022.3180799 – ident: e_1_2_12_33_1 doi: 10.1109/JSAC.2021.3118408 – ident: e_1_2_12_12_1 doi: 10.1109/JIOT.2021.3089054 – ident: e_1_2_12_53_1 – ident: e_1_2_12_4_1 – ident: e_1_2_12_22_1 doi: 10.1109/TMC.2022.3216837 – ident: e_1_2_12_23_1 doi: 10.1109/JIOT.2021.3085429 – ident: e_1_2_12_21_1 doi: 10.1109/TNSE.2022.3188571 – ident: e_1_2_12_50_1 doi: 10.1109/35.339880 – ident: e_1_2_12_49_1 doi: 10.1109/TWC.2007.06106 – ident: e_1_2_12_2_1 doi: 10.1017/9781108966559 – ident: e_1_2_12_37_1 doi: 10.1080/23307706.2017.1397554 – ident: e_1_2_12_7_1 doi: 10.1109/MSP.2020.2975749 – ident: e_1_2_12_16_1 doi: 10.1109/JSAC.2019.2904348 – ident: e_1_2_12_17_1 doi: 10.1109/INFOCOM.2019.8737464 – ident: e_1_2_12_32_1 doi: 10.1109/JSAC.2021.3126052 – ident: e_1_2_12_18_1 doi: 10.1109/LWC.2021.3069541 – ident: e_1_2_12_34_1 doi: 10.1109/TII.2022.3145010 – ident: e_1_2_12_43_1 doi: 10.1109/MCSE.2007.55 – volume-title: 5G NR: The Next Generation Wireless Access Technology year: 2020 ident: e_1_2_12_48_1 – ident: e_1_2_12_15_1 doi: 10.1109/ICCWorkshops49005.2020.9145118 – ident: e_1_2_12_31_1 doi: 10.1109/TIFS.2020.2988575 – ident: e_1_2_12_38_1 – ident: e_1_2_12_27_1 doi: 10.1561/0400000042 – ident: e_1_2_12_35_1 doi: 10.1109/LCOMM.2022.3167088 – ident: e_1_2_12_44_1 doi: 10.1007/978-3-662-53641-4_24 – ident: e_1_2_12_6_1 – ident: e_1_2_12_24_1 doi: 10.1109/JIOT.2021.3126828 – ident: e_1_2_12_47_1 doi: 10.1137/110830629 – ident: e_1_2_12_10_1 doi: 10.1109/TWC.2020.2974748 – ident: e_1_2_12_29_1 doi: 10.1109/JIOT.2021.3050163 – ident: e_1_2_12_42_1 doi: 10.1038/s41586-020-2649-2 – volume-title: Nonlinear Programming: Theory and Algorithms year: 2013 ident: e_1_2_12_51_1 – ident: e_1_2_12_39_1 – ident: e_1_2_12_28_1 – volume: 17 start-page: 1 issue: 83 year: 2016 ident: e_1_2_12_36_1 article-title: CVXPY: A Python‐embedded modeling language for convex optimization publication-title: J. Mach. Learn. Res. – ident: e_1_2_12_11_1 doi: 10.1109/TWC.2020.3037554 – ident: e_1_2_12_41_1 |
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| Snippet | 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... 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... 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... |
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| SubjectTerms | Computer Science with specialization in Computer Communication data privacy Datavetenskap med inriktning mot datorkommunikation differential privacy federated learning optimization scheduling wireless channels wireless communications |
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| Title | On differential privacy for federated learning in wireless systems with multiple base stations |
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