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Enhancing Privacy via Hierarchical Federated Learning [to appear]

Wainakh, Aidmar and Sanchez Guinea, Alejandro and Grube, Tim and Mühlhäuser, Max (2020):
Enhancing Privacy via Hierarchical Federated Learning [to appear].
In: IWPE'20-International Workshop on Privacy Engineering, Genova, Italy, 07.09.2020, [Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2020
Creators: Wainakh, Aidmar and Sanchez Guinea, Alejandro and Grube, Tim and Mühlhäuser, Max
Title: Enhancing Privacy via Hierarchical Federated Learning [to appear]
Language: English
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.

Divisions: 20 Department of Computer Science
20 Department of Computer Science > Telecooperation
Event Title: IWPE'20-International Workshop on Privacy Engineering
Event Location: Genova, Italy
Event Dates: 07.09.2020
Date Deposited: 09 Apr 2020 09:35
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[To appear]

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