IEICE TRANSACTIONS on Information
Frameworks for Privacy-Preserving Federated Learning
Summary :
In this paper, we explore privacy-preserving techniques in federated learning, including those can be used with both neural networks and decision trees. We begin by identifying how information can be leaked in federated learning, after which we present methods to address this issue by introducing two privacy-preserving frameworks that encompass many existing privacy-preserving federated learning (PPFL) systems. Through experiments with publicly available financial, medical, and Internet of Things datasets, we demonstrate the effectiveness of privacy-preserving federated learning and its potential to develop highly accurate, secure, and privacy-preserving machine learning systems in real-world scenarios. The findings highlight the importance of considering privacy in the design and implementation of federated learning systems and suggest that privacy-preserving techniques are essential in enabling the development of effective and practical machine learning systems.
- Publication
- IEICE TRANSACTIONS on Information Vol.E107-D No.1 pp.2-12
- Publication Date
- 2024/01/01
- Publicized
- 2023/09/25
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2023MUI0001
- Type of Manuscript
- Special Section INVITED PAPER (Special Section on Enriched Multimedia — Media technologies opening up the future —)
- Category
Authors
Le Trieu PHONG
National Institute of Information and Communications Technology (NICT)
Tran Thi PHUONG
National Institute of Information and Communications Technology (NICT),KDDI Research, Inc.
Lihua WANG
National Institute of Information and Communications Technology (NICT)
Seiichi OZAWA
Kobe University
Keyword
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Le Trieu PHONG, Tran Thi PHUONG, Lihua WANG, Seiichi OZAWA, "Frameworks for Privacy-Preserving Federated Learning" in IEICE TRANSACTIONS on Information,
vol. E107-D, no. 1, pp. 2-12, January 2024, doi: 10.1587/transinf.2023MUI0001.
Abstract: In this paper, we explore privacy-preserving techniques in federated learning, including those can be used with both neural networks and decision trees. We begin by identifying how information can be leaked in federated learning, after which we present methods to address this issue by introducing two privacy-preserving frameworks that encompass many existing privacy-preserving federated learning (PPFL) systems. Through experiments with publicly available financial, medical, and Internet of Things datasets, we demonstrate the effectiveness of privacy-preserving federated learning and its potential to develop highly accurate, secure, and privacy-preserving machine learning systems in real-world scenarios. The findings highlight the importance of considering privacy in the design and implementation of federated learning systems and suggest that privacy-preserving techniques are essential in enabling the development of effective and practical machine learning systems.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023MUI0001/_p
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@ARTICLE{e107-d_1_2,
author={Le Trieu PHONG, Tran Thi PHUONG, Lihua WANG, Seiichi OZAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Frameworks for Privacy-Preserving Federated Learning},
year={2024},
volume={E107-D},
number={1},
pages={2-12},
abstract={In this paper, we explore privacy-preserving techniques in federated learning, including those can be used with both neural networks and decision trees. We begin by identifying how information can be leaked in federated learning, after which we present methods to address this issue by introducing two privacy-preserving frameworks that encompass many existing privacy-preserving federated learning (PPFL) systems. Through experiments with publicly available financial, medical, and Internet of Things datasets, we demonstrate the effectiveness of privacy-preserving federated learning and its potential to develop highly accurate, secure, and privacy-preserving machine learning systems in real-world scenarios. The findings highlight the importance of considering privacy in the design and implementation of federated learning systems and suggest that privacy-preserving techniques are essential in enabling the development of effective and practical machine learning systems.},
keywords={},
doi={10.1587/transinf.2023MUI0001},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Frameworks for Privacy-Preserving Federated Learning
T2 - IEICE TRANSACTIONS on Information
SP - 2
EP - 12
AU - Le Trieu PHONG
AU - Tran Thi PHUONG
AU - Lihua WANG
AU - Seiichi OZAWA
PY - 2024
DO - 10.1587/transinf.2023MUI0001
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E107-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2024
AB - In this paper, we explore privacy-preserving techniques in federated learning, including those can be used with both neural networks and decision trees. We begin by identifying how information can be leaked in federated learning, after which we present methods to address this issue by introducing two privacy-preserving frameworks that encompass many existing privacy-preserving federated learning (PPFL) systems. Through experiments with publicly available financial, medical, and Internet of Things datasets, we demonstrate the effectiveness of privacy-preserving federated learning and its potential to develop highly accurate, secure, and privacy-preserving machine learning systems in real-world scenarios. The findings highlight the importance of considering privacy in the design and implementation of federated learning systems and suggest that privacy-preserving techniques are essential in enabling the development of effective and practical machine learning systems.
ER -
English
