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

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.

@article{sorbera2025_2504.00563,
  title={ Adaptive Federated Learning with Functional Encryption: A Comparison of Classical and Quantum-safe Options },
  author={ Enrico Sorbera and Federica Zanetti and Giacomo Brandi and Alessandro Tomasi and Roberto Doriguzzi-Corin and Silvio Ranise },
  journal={arXiv preprint arXiv:2504.00563},
  year={ 2025 }
}