A Comparative Study of Privacy-Preserving Techniques in Federated Learning: A Performance and Security Analysis

A Comparative Study of Privacy-Preserving Techniques in Federated Learning: A Performance and Security Analysis

Federated learning (FL) is a machine learning technique where clients exchange only local model updates with a central server that combines them to create a global model after local training. While FL offers privacy benefits through local training, privacy-preserving strategies are needed since mode...

Full description

Saved in:
Bibliographic Details
Main Authors: Eman Shalabi, Walid Khedr, Ehab Rushdy, Ahmad Salah
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Information
Subjects:
federated learning
privacy preserving
homomorphic encryption
secure multi-party computation
private aggregation of teacher ensembles
poisoning attacks
Online Access:https://www.mdpi.com/2078-2489/16/3/244
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2078-2489/16/3/244

Similar Items