Comparative Analysis of Membership Inference Attacks in Federated and Centralized Learning

Comparative Analysis of Membership Inference Attacks in Federated and Centralized Learning

The vulnerability of machine learning models to membership inference attacks, which aim to determine whether a specific record belongs to the training dataset, is explored in this paper. Federated learning allows multiple parties to independently train a model without sharing or centralizing their d...

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Bibliographic Details
Main Authors:	Ali Abbasi Tadi, Saroj Dayal, Dima Alhadidi, Noman Mohammed
Format:	Article
Language:	English
Published:	MDPI AG 2023-11-01
Series:	Information
Subjects:	federated learning membership inference attack privacy machine learning
Online Access:	https://www.mdpi.com/2078-2489/14/11/620

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