Differential Privacy Federated Learning: A Comprehensive Review

Federated Learning (FL) has received a lot of attention lately when it comes to protecting data privacy, especially in industries with sensitive data like healthcare, banking, and the Internet of Things (IoT). However, although FL protects privacy by not sharing raw data, the information transfer du...

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Veröffentlicht in:International journal of advanced computer science & applications Jg. 15; H. 7
Hauptverfasser: Shan, Fangfang, Mao, Shiqi, Lu, Yanlong, Li, Shuaifeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: West Yorkshire Science and Information (SAI) Organization Limited 2024
Schlagworte:
Adaptive sampling
Algorithms
Federated learning
Information transfer
Internet of Things
Machine learning
Privacy
Query processing
Random noise
ISSN:2158-107X, 2156-5570
Online-Zugang:Volltext
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Zusammenfassung:Federated Learning (FL) has received a lot of attention lately when it comes to protecting data privacy, especially in industries with sensitive data like healthcare, banking, and the Internet of Things (IoT). However, although FL protects privacy by not sharing raw data, the information transfer during its model update process can still potentially leak user privacy. Differential Privacy (DP), as an advanced privacy protection technology, introduces random noise during data queries or model updates, further enhancing the privacy protection capability of Federated Learning. This paper delves into the theory, technology, development, and future research recommendations of Differential Privacy Federated Learning (DP-FL). Firstly, the article introduces the basic concepts of Federated Learning, including synchronous and asynchronous optimization algorithms, and explains the fundamentals of Differential Privacy, including centralized and local DP mechanisms. Then, the paper discusses in detail the application of DP in Federated Learning under different gradient clipping strategies, including fixed clipping and adaptive clipping methods, and explores the application of user-level and sample-level DP in Federated Learning. Finally, the paper discusses future research directions for DP-FL, emphasizing advancements in asynchronous DP-FL and personalized DP-FL.
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2024.0150722