Deep federated learning hybrid optimization model based on encrypted aligned data

•Improving the quality of Federal Learning encrypted alignment data.•Use Gaussian mixture clustering to cluster samples and set a threshold to filter samples.•Use the encrypted sample attribute searching algorithm to fill in the missing value of the sample.•Design the combination model of variation...

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Veröffentlicht in:	Pattern recognition Jg. 148; S. 110193
Hauptverfasser:	Zhao, Zhongnan, Liang, Xiaoliang, Huang, Hai, Wang, Kun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.04.2024
Schlagworte:	Encrypted aligned data, Privacy protection Federated learning Gaussian Mixture Model Variational AutoEncoder Encrypted aligned data, Privacy protection Gaussian Mixture Model Federated learning Variational AutoEncoder
ISSN:	0031-3203
Online-Zugang:	Volltext
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Zusammenfassung:	•Improving the quality of Federal Learning encrypted alignment data.•Use Gaussian mixture clustering to cluster samples and set a threshold to filter samples.•Use the encrypted sample attribute searching algorithm to fill in the missing value of the sample.•Design the combination model of variation auto-encoder Gaussian hybrid clustering and federated learning. Federated learning can achieve multi-party data-collaborative applications while safeguarding personal privacy. However, the process often leads to a decline in the quality of sample data due to a substantial amount of missing encrypted aligned data, and there is a lack of research on how to improve the model learning effect by increasing the number of samples of encrypted aligned data in federated learning. Therefore, this paper integrates the functional characteristics of deep learning models and proposes a Variational AutoEncoder Gaussian Mixture Model Clustering Vertical Federated Learning Model (VAEGMMC-VFL), which leverages the feature extraction capability of the autoencoder and the clustering and pattern discovery capabilities of Gaussian mixture clustering on diverse datasets to further explore a large number of potentially usable samples. Firstly, the Variational AutoEncoder is used to achieve dimensionality reduction and sample feature reconstruction of high-dimensional data samples. Subsequently, Gaussian mixture clustering is further employed to partition the dataset into multiple potential Gaussian-distributed clusters and filter the sample data using thresholding. Additionally, the paper introduces a labeled sample attribute value finding algorithm to fill in attribute values for encrypted unaligned samples that meet the requirements, allowing for the full recovery of encrypted unaligned data. In the experimental section, the paper selects four sets of datasets from different industries and compares the proposed method with three federated learning clustering methods in terms of clustering loss, reconstruction loss, and other metrics. Tests on precision, accuracy, recall, ROC curve, and F1-score indicate that the proposed method outperforms similar approaches.
ISSN:	0031-3203
DOI:	10.1016/j.patcog.2023.110193