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

TL;DR

QuanCrypt-FL introduces a privacy-preserving federated learning framework that combines quantization and pruning with homomorphic encryption to reduce computational costs and enhance attack resistance, maintaining accuracy across datasets.

Contribution

It proposes a novel combination of low-bit quantization, pruning, and mean-based clipping to improve efficiency and security in homomorphically encrypted federated learning.

Findings

Achieves up to 9x faster encryption and 16x faster decryption.

Reduces training time by up to 3x.

Maintains comparable accuracy to vanilla federated learning.

Abstract

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.