Adaptive Federated Learning with Functional Encryption: A Comparison of Classical and Quantum-safe Options

• URL: http://arxiv.org/abs/2504.00563v1
• Date: Tue, 01 Apr 2025 09:18:06 GMT
• Title: Adaptive Federated Learning with Functional Encryption: A Comparison of Classical and Quantum-safe Options
• Authors: Enrico Sorbera, Federica Zanetti, Giacomo Brandi, Alessandro Tomasi, Roberto Doriguzzi-Corin, Silvio Ranise,
• Abstract summary: Federated Learning (FL) is a collaborative method for training machine learning models.<n>FL is vulnerable to reconstruction attacks that exploit shared parameters to reveal private training data.<n>We apply MIFE to a recent FL implementation for training ML-based network intrusion detection systems.
• Score: 37.78083560410636
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Federated Learning (FL) is a collaborative method for training machine learning models while preserving the confidentiality of the participants' training data. Nevertheless, FL is vulnerable to reconstruction attacks that exploit shared parameters to reveal private training data. In this paper, we address this issue in the cybersecurity domain by applying Multi-Input Functional Encryption (MIFE) to a recent FL implementation for training ML-based network intrusion detection systems. We assess both classical and post-quantum solutions in terms of memory cost and computational overhead in the FL process, highlighting their impact on convergence time.

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