# QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning

arXiv: https://arxiv.org/abs/2411.05260 · HTML: https://mlsanity.com/paper/2411.05260/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 72 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Builds on (most cited first, 13 of 13)

- [iDLG: Improved Deep Leakage from Gradients](https://mlsanity.com/paper/2001.02610/) (2020)
- [Local and Central Differential Privacy for Robustness and Privacy in Federated Learning](https://mlsanity.com/paper/2009.03561/) (2020)
- [LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy](https://mlsanity.com/paper/2007.15789/) (2020)
- [TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption](https://mlsanity.com/paper/2104.03152/) (2021)
- [Cross-Silo Federated Learning: Challenges and Opportunities](https://mlsanity.com/paper/2206.12949/) (2022)
- [FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System](https://mlsanity.com/paper/2303.10837/) (2023)
- [Learning Differentially Private Recurrent Language Models](https://mlsanity.com/paper/1710.06963/) (2017)
- [Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning](https://mlsanity.com/paper/2004.12571/) (2020)
- [FPGA-Based Hardware Accelerator of Homomorphic Encryption for Efficient Federated Learning](https://mlsanity.com/paper/2007.10560/) (2020)
- [MIBench: A Comprehensive Framework for Benchmarking Model Inversion Attack and Defense](https://mlsanity.com/paper/2410.05159/) (2024)
- [Combating Client Dropout in Federated Learning via Friend Model Substitution](https://mlsanity.com/paper/2205.13222/) (2022)
- [Local Model Reconstruction Attacks in Federated Learning and their Uses](https://mlsanity.com/paper/2210.16205/) (2022)
- [CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness](https://mlsanity.com/paper/2409.13133/) (2024)

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

- [A Privacy-Preserving Federated Learning Method with Homomorphic Encryption in Omics Data](https://mlsanity.com/paper/2511.06064/) (2025)
- [FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs](https://mlsanity.com/paper/2512.23235/) (2025)

## Abstract (arXiv)

Federated Learning has emerged as a leading approach for decentralized machine learning, enabling multiple clients to collaboratively train a shared model without exchanging private data. While FL enhances data privacy, it remains vulnerable to inference attacks, such as gradient inversion and membership inference, during both training and inference phases. Homomorphic Encryption provides a promising solution by encrypting model updates to protect against such attacks, but it introduces substantial communication overhead, slowing down training and increasing computational costs. To address these challenges, we propose QuanCrypt-FL, a novel algorithm that combines low-bit quantization and pruning techniques to enhance protection against attacks while significantly reducing computational costs during training. Further, we propose and implement mean-based clipping to mitigate quantization overflow or errors. By integrating these methods, QuanCrypt-FL creates a communication-efficient FL framework that ensures privacy protection with minimal impact on model accuracy, thereby improving both computational efficiency and attack resilience. We validate our approach on MNIST, CIFAR-10, and CIFAR-100 datasets, demonstrating superior performance compared to state-of-the-art methods. QuanCrypt-FL consistently outperforms existing method and matches Vanilla-FL in terms of accuracy across varying client. Further, QuanCrypt-FL achieves up to 9x faster encryption, 16x faster decryption, and 1.5x faster inference compared to BatchCrypt, with training time reduced by up to 3x.