Efficient DP-FL: Efficient Differential Privacy Federated Learning Based on Early Stopping Mechanism.
In: Computer Systems Science & Engineering, Jg. 48 (2024), Heft 1, S. 247-265
Online
academicJournal
Zugriff:
Federated learning is a distributed machine learning framework that solves data security and data island problems faced by artificial intelligence. However, federated learning frameworks are not always secure, and attackers can attack customer privacy information by analyzing parameters in the training process of federated learning models. To solve the problems of data security and availability during federated learning training, this paper proposes an Efficient Differential Privacy Federated Learning Algorithm based on early stopping mechanism (EfficientDP-FL). This method inherits the advantages of differential privacy and federated learning and improves the performance of model training while protecting the parameter information uploaded by the client during the training process. Specifically, in the federated learning framework, this article uses an adaptive DP-FL method for gradient descent training, which makes the model converge faster than traditional stochastic gradient descent. In addition, due to model convergence, noise should be reduced accordingly. This paper introduces an early stopping mechanism to improve data availability. This paper demonstrates the performance improvement of the Efficient DP-FL algorithm through simulation experiments on real MNIST and Fashion-MNIST datasets. Experimental showthat the efficient DP-FL algorithm is significantly superior to other algorithms. [ABSTRACT FROM AUTHOR]
Copyright of Computer Systems Science & Engineering is the property of Tech Science Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Titel: |
Efficient DP-FL: Efficient Differential Privacy Federated Learning Based on Early Stopping Mechanism.
|
---|---|
Autor/in / Beteiligte Person: | Jiao, Sanxiu ; LecaiCai ; Meng, Jintao ; Zhao, Yue ; Cheng, Kui |
Link: | |
Zeitschrift: | Computer Systems Science & Engineering, Jg. 48 (2024), Heft 1, S. 247-265 |
Veröffentlichung: | 2024 |
Medientyp: | academicJournal |
ISSN: | 0267-6192 (print) |
DOI: | 10.32604/csse.2023.040194 |
Schlagwort: |
|
Sonstiges: |
|