HP_FLAP: homomorphic and polymorphic federated learning aggregation of parameters framework

HP_FLAP: homomorphic and polymorphic federated learning aggregation of parameters framework

Abstract Protecting user privacy is essential in machine learning research, especially in the context of data collection. Federated learning (FL), which trains models across decentralized devices without sharing raw data, has emerged as a promising solution. However, FL is still vulnerable to securi...

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Bibliographic Details
Main Authors: Mohammad Moshawrab, Mehdi Adda, Abdenour Bouzouane, Hussein Ibrahim, Ali Raad
Format: Article
Language:English
Published: SpringerOpen 2025-06-01
Series:Cybersecurity
Subjects:
Federated machine learning
Aggregation algorithms
Polymorphic encryption
Homomorphic encryption
Security
Privacy
Online Access:https://doi.org/10.1186/s42400-024-00341-6
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https://doi.org/10.1186/s42400-024-00341-6

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