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 |
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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. |
Zusätzliche Informationen: | Preprint |
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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