dc.contributor.author | Gogineni, Vinay Chakravarthi | |
dc.contributor.author | Moradi, Ashkan | |
dc.contributor.author | Kumar Dasanadoddi Venkategowda, Naveen | |
dc.contributor.author | Werner, Stefan | |
dc.date.accessioned | 2023-01-30T11:10:46Z | |
dc.date.available | 2023-01-30T11:10:46Z | |
dc.date.created | 2022-10-19T21:25:41Z | |
dc.date.issued | 2022 | |
dc.identifier.isbn | 978-1-7377497-2-1 | |
dc.identifier.uri | https://hdl.handle.net/11250/3047038 | |
dc.description.abstract | This paper presents a private-partial distributed least mean square (PP-DLMS) algorithm that offers energy efficiency while preserving privacy and is suitable for applications with limited resources and strict security requirements. The proposed PP-DLMS allows every agent to exchange only a fraction of their perturbed data with neighbors during the collaboration process to minimize communication costs and guarantee privacy simultaneously. In order to understand how partial-sharing of perturbed data affects the learning performance, we conduct mean convergence analysis. Moreover, to investigate the privacy-preserving properties of the proposed algorithm, we characterize agent privacy in the presence of an honest-but-curious (HBC) adversary. Analytical results show that the proposed PP-DLMS is resilient against an HBC adversary by providing a fair energy-privacy trade-off compared to the conventional LMS algorithm. Numerical simulations corroborate the analytical findings. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2022 25th International Conference on Information Fusion - FUSION | |
dc.title | Communication-efficient and privacy-aware distributed LMS algorithm | en_US |
dc.title.alternative | Communication-efficient and privacy-aware distributed LMS algorithm | en_US |
dc.type | Chapter | en_US |
dc.description.version | acceptedVersion | en_US |
dc.rights.holder | © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.identifier.doi | 10.23919/FUSION49751.2022.9841380 | |
dc.identifier.cristin | 2062976 | |
dc.relation.project | Norges forskningsråd: 300102 | en_US |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |