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Enhancing Privacy via Hierarchical Federated Learning

Wainakh, Aidmar ; Sanchez Guinea, Alejandro ; Grube, Tim ; Mühlhäuser, Max (2020)
Enhancing Privacy via Hierarchical Federated Learning.
Report, Bibliographie

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Kurzbeschreibung (Abstract)

Federated learning suffers from several privacy-related issues that expose the participants to various threats. A number of these issues are aggravated by the centralized architecture of federated learning. In this paper, we discuss applying federated learning on a hierarchical architecture as a potential solution. We introduce the opportunities for more flexible decentralized control over the training process and its impact on the participants' privacy. Furthermore, we investigate possibilities to enhance the efficiency and effectiveness of defense and verification methods.

Typ des Eintrags: Report
Erschienen: 2020
Autor(en): Wainakh, Aidmar ; Sanchez Guinea, Alejandro ; Grube, Tim ; Mühlhäuser, Max
Art des Eintrags: Bibliographie
Titel: Enhancing Privacy via Hierarchical Federated Learning
Sprache: Englisch
Publikationsjahr: 23 April 2020
Verlag: arXiv
Reihe: Cryptography and Security
URL / URN: https://arxiv.org/abs/2004.11361
Kurzbeschreibung (Abstract):

Federated learning suffers from several privacy-related issues that expose the participants to various threats. A number of these issues are aggravated by the centralized architecture of federated learning. In this paper, we discuss applying federated learning on a hierarchical architecture as a potential solution. We introduce the opportunities for more flexible decentralized control over the training process and its impact on the participants' privacy. Furthermore, we investigate possibilities to enhance the efficiency and effectiveness of defense and verification methods.

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Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Telekooperation
Hinterlegungsdatum: 16 Feb 2022 09:51
Letzte Änderung: 22 Jul 2024 12:04
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