Zhang, Wen ; Fan, Yufan ; Pesavento, Marius (2023)
Preserving Privacy in Distributed LASSO.
9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. Herradura, Costa Rica (10.12. - 13.12.2023)
doi: 10.1109/CAMSAP58249.2023.10403477
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In this paper, we extend the Soft-Thresholding with Exact Line search Algorithm (STELA) to solve the LASSO problem in a fully decentralized manner, where each agent solves its local minimization problem, and cooperates only with its neighbors to update the local solution. Moreover, the privacy of the local data is maintained during the communication of agents via the privacy-preserving average consensus (PPAC) approach which avoids revealing local information from other agents as well as potential eavesdroppers. We examine the proposed algorithm with synthetic data. Simulation results show that with a similar privacy level, the proposed algorithm has a faster convergence speed and better accuracy compared to the state-of-the-art privacy-preserving Primal-Dual Method of Multipliers (p-PDMM) algorithm.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Zhang, Wen ; Fan, Yufan ; Pesavento, Marius |
| Art des Eintrags: | Bibliographie |
| Titel: | Preserving Privacy in Distributed LASSO |
| Sprache: | Englisch |
| Publikationsjahr: | 14 Dezember 2023 |
| Verlag: | IEEE |
| Buchtitel: | 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) |
| Veranstaltungstitel: | 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing |
| Veranstaltungsort: | Herradura, Costa Rica |
| Veranstaltungsdatum: | 10.12. - 13.12.2023 |
| DOI: | 10.1109/CAMSAP58249.2023.10403477 |
| Kurzbeschreibung (Abstract): | In this paper, we extend the Soft-Thresholding with Exact Line search Algorithm (STELA) to solve the LASSO problem in a fully decentralized manner, where each agent solves its local minimization problem, and cooperates only with its neighbors to update the local solution. Moreover, the privacy of the local data is maintained during the communication of agents via the privacy-preserving average consensus (PPAC) approach which avoids revealing local information from other agents as well as potential eavesdroppers. We examine the proposed algorithm with synthetic data. Simulation results show that with a similar privacy level, the proposed algorithm has a faster convergence speed and better accuracy compared to the state-of-the-art privacy-preserving Primal-Dual Method of Multipliers (p-PDMM) algorithm. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Nachrichtentechnische Systeme |
| Hinterlegungsdatum: | 03 Apr 2024 12:58 |
| Letzte Änderung: | 03 Apr 2024 12:58 |
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