Autoencoder for Detecting Malicious Updates in Differentially Private Federated Learning

Lucia Alonso; Mina Alishahi

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Paper

Autoencoder for Detecting Malicious Updates in Differentially Private Federated Learning

Topics: Data Protection; Database Security and Privacy; Machine Learning Security and Privacy; Privacy

In Proceedings of the 21st International Conference on Security and Cryptography SECRYPT - Volume 1, 467-474, 2024 , Dijon, France

Authors: Lucia Alonso ¹ and Mina Alishahi ²

Affiliations: ¹ Informatics Institute, University of Amsterdam, The Netherlands ; ² Department of Computer Science, Open Universiteit, The Netherlands

Keyword(s): Federated Learning, Differential Privacy, Autoencoder, Anomaly Detection.

Abstract: Differentially Private Federated Learning (DP-FL) is a novel machine learning paradigm that integrates federated learning with the principles of differential privacy. In DP-FL, a global model is trained across decentralized devices or servers, each holding local data samples, without the need to exchange raw data. This approach ensures data privacy by adding noise to the model updates before aggregation, thus preventing any individual contributor’s data from being compromised. However, ensuring the integrity of the model updates from these contributors is paramount. This research explores the application of autoencoders as a means to detect anomalous or fraudulent updates from contributors in DP-FL. By leveraging the reconstruction errors generated by autoencoders, this study assesses their effectiveness in identifying anomalies while also discussing potential limitations of this approach.

CC BY-NC-ND 4.0

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Paper citation in several formats:

Alonso, L. and Alishahi, M. (2024). Autoencoder for Detecting Malicious Updates in Differentially Private Federated Learning. In Proceedings of the 21st International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-709-2; ISSN 2184-7711, SciTePress, pages 467-474. DOI: 10.5220/0012766700003767

@conference{secrypt24,
author={Lucia Alonso and Mina Alishahi},
title={Autoencoder for Detecting Malicious Updates in Differentially Private Federated Learning},
booktitle={Proceedings of the 21st International Conference on Security and Cryptography - SECRYPT},
year={2024},
pages={467-474},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012766700003767},
isbn={978-989-758-709-2},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 21st International Conference on Security and Cryptography - SECRYPT
TI - Autoencoder for Detecting Malicious Updates in Differentially Private Federated Learning
SN - 978-989-758-709-2
IS - 2184-7711
AU - Alonso, L.
AU - Alishahi, M.
PY - 2024
SP - 467
EP - 474
DO - 10.5220/0012766700003767
PB - SciTePress