Enhancing Differential Privacy for Federated Learning at Scale

Federated learning (FL) is an emerging technique that trains machine learning models across multiple de-centralized systems. It enables local devices to collaboratively learn a model by aggregating locally computed updates via a server. Privacy is a core aspect of FL, and recent works in this area a...

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Autores principales:	Chunghun Baek, Sungwook Kim, Dongkyun Nam, Jihoon Park
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Differential privacy federated learning user dropout noise calibration Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/ca4f5cebcb204fb6adf064ce48a9d83f
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id	oai:doaj.org-article:ca4f5cebcb204fb6adf064ce48a9d83f
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spelling	oai:doaj.org-article:ca4f5cebcb204fb6adf064ce48a9d83f2021-11-18T00:11:10ZEnhancing Differential Privacy for Federated Learning at Scale2169-353610.1109/ACCESS.2021.3124020https://doaj.org/article/ca4f5cebcb204fb6adf064ce48a9d83f2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9592806/https://doaj.org/toc/2169-3536Federated learning (FL) is an emerging technique that trains machine learning models across multiple de-centralized systems. It enables local devices to collaboratively learn a model by aggregating locally computed updates via a server. Privacy is a core aspect of FL, and recent works in this area are advancing the privacy guarantee of an FL network. To ensure rigorous privacy guarantee for FL, prior works have focused on methods to securely aggregate local updates and provide differential privacy (DP). In this paper, we investigate a new privacy risk for FL. Specifically, FL may frequently encounter unexpected user dropouts because it is implemented over a large-scale network. We first observe that user dropouts of an FL network may lead to failure in achieving the desired level of privacy protection, i.e., over-consumption of the privacy budget. Subsequently, we develop a DP mechanism robust to user dropouts by dynamically calibrating noise with account of the dropout rate. We evaluate the proposed technique to train convolutional neural network models on MNIST and FEMNIST datasets over a simulated FL network. Our results show that our approach significantly improves privacy guarantee for user dropouts compared to existing DP algorithms on FL networks.Chunghun BaekSungwook KimDongkyun NamJihoon ParkIEEEarticleDifferential privacyfederated learninguser dropoutnoise calibrationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 148090-148103 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Differential privacy federated learning user dropout noise calibration Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Differential privacy federated learning user dropout noise calibration Electrical engineering. Electronics. Nuclear engineering TK1-9971 Chunghun Baek Sungwook Kim Dongkyun Nam Jihoon Park Enhancing Differential Privacy for Federated Learning at Scale
description	Federated learning (FL) is an emerging technique that trains machine learning models across multiple de-centralized systems. It enables local devices to collaboratively learn a model by aggregating locally computed updates via a server. Privacy is a core aspect of FL, and recent works in this area are advancing the privacy guarantee of an FL network. To ensure rigorous privacy guarantee for FL, prior works have focused on methods to securely aggregate local updates and provide differential privacy (DP). In this paper, we investigate a new privacy risk for FL. Specifically, FL may frequently encounter unexpected user dropouts because it is implemented over a large-scale network. We first observe that user dropouts of an FL network may lead to failure in achieving the desired level of privacy protection, i.e., over-consumption of the privacy budget. Subsequently, we develop a DP mechanism robust to user dropouts by dynamically calibrating noise with account of the dropout rate. We evaluate the proposed technique to train convolutional neural network models on MNIST and FEMNIST datasets over a simulated FL network. Our results show that our approach significantly improves privacy guarantee for user dropouts compared to existing DP algorithms on FL networks.
format	article
author	Chunghun Baek Sungwook Kim Dongkyun Nam Jihoon Park
author_facet	Chunghun Baek Sungwook Kim Dongkyun Nam Jihoon Park
author_sort	Chunghun Baek
title	Enhancing Differential Privacy for Federated Learning at Scale
title_short	Enhancing Differential Privacy for Federated Learning at Scale
title_full	Enhancing Differential Privacy for Federated Learning at Scale
title_fullStr	Enhancing Differential Privacy for Federated Learning at Scale
title_full_unstemmed	Enhancing Differential Privacy for Federated Learning at Scale
title_sort	enhancing differential privacy for federated learning at scale
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/ca4f5cebcb204fb6adf064ce48a9d83f
work_keys_str_mv	AT chunghunbaek enhancingdifferentialprivacyforfederatedlearningatscale AT sungwookkim enhancingdifferentialprivacyforfederatedlearningatscale AT dongkyunnam enhancingdifferentialprivacyforfederatedlearningatscale AT jihoonpark enhancingdifferentialprivacyforfederatedlearningatscale
_version_	1718425191552385024

Enhancing Differential Privacy for Federated Learning at Scale

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