A Differential Privacy Strategy Based on Local Features of Non-Gaussian Noise in Federated Learning

A Differential Privacy Strategy Based on Local Features of Non-Gaussian Noise in Federated Learning

As an emerging artificial intelligence technology, federated learning plays a significant role in privacy preservation in machine learning, although its main objective is to prevent peers from peeping data. However, attackers from the outside can steal metadata in transit and through data reconstruc...

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Bibliographic Details
Main Authors: Xinyi Wang, Jincheng Wang, Xue Ma, Chenglin Wen
Format: Article
Language:English
Published: MDPI AG 2022-03-01
Series:Sensors
Subjects:
federated learning (FL)
differential privacy
Kalman filter
non-Gaussian noise
Online Access:https://www.mdpi.com/1424-8220/22/7/2424

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