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

arXiv: https://arxiv.org/abs/2504.00563 · HTML: https://mlsanity.com/paper/2504.00563/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2025.

## Built on by (most cited first, 2 of 2)

- [PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework](https://mlsanity.com/paper/2505.01866/) (2025)
- [AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing](https://mlsanity.com/paper/2507.07316/) (2025)

## Abstract (arXiv)

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.