Survey of Privacy Threats and Countermeasures in Federated Learning

Fuente: arXiv
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Main Authors: Hayashitani, Masahiro, Mori, Junki, Teranishi, Isamu
Format: Preprint
Published: 2024
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author Hayashitani, Masahiro
Mori, Junki
Teranishi, Isamu
author_facet Hayashitani, Masahiro
Mori, Junki
Teranishi, Isamu
contents Federated learning is widely considered to be as a privacy-aware learning method because no training data is exchanged directly between clients. Nevertheless, there are threats to privacy in federated learning, and privacy countermeasures have been studied. However, we note that common and unique privacy threats among typical types of federated learning have not been categorized and described in a comprehensive and specific way. In this paper, we describe privacy threats and countermeasures for the typical types of federated learning; horizontal federated learning, vertical federated learning, and transfer federated learning.
format Preprint
id arxiv_https___arxiv_org_abs_2402_00342
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Survey of Privacy Threats and Countermeasures in Federated Learning
Hayashitani, Masahiro
Mori, Junki
Teranishi, Isamu
Machine Learning
Cryptography and Security
Federated learning is widely considered to be as a privacy-aware learning method because no training data is exchanged directly between clients. Nevertheless, there are threats to privacy in federated learning, and privacy countermeasures have been studied. However, we note that common and unique privacy threats among typical types of federated learning have not been categorized and described in a comprehensive and specific way. In this paper, we describe privacy threats and countermeasures for the typical types of federated learning; horizontal federated learning, vertical federated learning, and transfer federated learning.
title Survey of Privacy Threats and Countermeasures in Federated Learning
topic Machine Learning
Cryptography and Security
url https://arxiv.org/abs/2402.00342