A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets

Autor:	Arévalo, Irina, Salmeron, Jose L.
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Cryptography and Security Computer Science - Distributed Parallel and Cluster Computing
Zdroj:	IEEE Transactions on Emerging Topics in Computing, 2023
Druh dokumentu:	Working Paper
Popis:	Federated Learning is a machine learning approach that enables the training of a deep learning model among several participants with sensitive data that wish to share their own knowledge without compromising the privacy of their data. In this research, the authors employ a secured Federated Learning method with an additional layer of privacy and proposes a method for addressing the non-IID challenge. Moreover, differential privacy is compared with chaotic-based encryption as layer of privacy. The experimental approach assesses the performance of the federated deep learning model with differential privacy using both IID and non-IID data. In each experiment, the Federated Learning process improves the average performance metrics of the deep neural network, even in the case of non-IID data.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.10145 Zobrazit plný text záznamu View this record from Arxiv