[Paper Review] Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving ML

Siddhant Dutta, Pavana P Karanth+6 more|arXiv (Cornell University)|Sep 14, 2024

Privacy-Preserving Technologies in Data4 citations

TL;DR

This paper proposes a novel Federated Quantum Neural Network (FHE-FedQNN) that integrates Fully Homomorphic Encryption (FHE) and quantum computing within a federated learning framework to enhance privacy and efficiency. By encrypting model updates with FHE and leveraging quantum circuits for local computation, the approach achieves strong privacy guarantees with minimal performance overhead, demonstrating improved generalization and scalability in privacy-preserving machine learning.

ABSTRACT

The widespread deployment of products powered by machine learning models is raising concerns around data privacy and information security worldwide. To address this issue, Federated Learning was first proposed as a privacy-preserving alternative to conventional methods that allow multiple learning clients to share model knowledge without disclosing private data. A complementary approach known as Fully Homomorphic Encryption (FHE) is a quantum-safe cryptographic system that enables operations to be performed on encrypted weights. However, implementing mechanisms such as these in practice often comes with significant computational overhead and can expose potential security threats. Novel computing paradigms, such as analog, quantum, and specialized digital hardware, present opportunities for implementing privacy-preserving machine learning systems while enhancing security and mitigating performance loss. This work instantiates these ideas by applying the FHE scheme to a Federated Learning Neural Network architecture that integrates both classical and quantum layers.

Motivation & Objective

• To address growing concerns over data privacy and security in federated machine learning.
• To mitigate the computational and communication overheads inherent in traditional federated learning.
• To enhance privacy through quantum-safe encryption using Fully Homomorphic Encryption (FHE).
• To explore the integration of quantum computing with federated learning for accelerated local training.
• To evaluate the performance trade-offs between privacy, accuracy, and training efficiency in hybrid classical-quantum FL systems.

Proposed method

• Proposes a hybrid Federated Learning architecture with both classical and quantum neural network layers.
• Applies Fully Homomorphic Encryption (FHE) to encrypt model updates before transmission to the central server.
• Employs quantum circuits for local model training on clients, leveraging quantum speedup for matrix operations and gradient computation.
• Uses tensor network simulations to emulate quantum circuits on classical hardware for practical implementation and reproducibility.
• Employs a federated averaging strategy where encrypted model updates are aggregated without decryption.
• Validates the framework on CIFAR-10, Brain MRI, and PCOS datasets across 20 rounds with 20 clients and 10 epochs per round.

Experimental results

Research questions

• RQ1 How does integrating FHE with quantum federated learning affect model accuracy and training time?
• RQ2 Can quantum computing layers in federated learning reduce local computation overhead while preserving privacy?
• RQ3 What is the performance impact of FHE on model generalization in federated quantum neural networks?
• RQ4 How do FHE-FedQNN and standard FedNN models compare in terms of test accuracy and loss across diverse datasets?
• RQ5 To what extent can quantum simulation on classical hardware replicate the behavior of real quantum devices in this hybrid FL framework?

Key findings

• The FHE-FedQNN model achieved 99.60% training accuracy and 88.75% test accuracy on the Brain MRI dataset, outperforming the standard FedNN in generalization despite higher test loss.
• On CIFAR-10, the FHE-FedQNN model reached 100% training accuracy and 68.53% test accuracy with a test loss of 1.322 and training time of 136.4 minutes.
• The FHE-FedQNN model showed improved generalizability compared to standard FedNN, with higher test accuracy on Brain MRI (88.4% vs. 90.36%) and PCOS (64.11% vs. 65.37%) despite higher test loss.
• The integration of quantum layers and FHE did not significantly increase training time, with the FHE-FedQNN model completing in 136.4 minutes on CIFAR-10, compared to 128.9 minutes for standard FedNN.
• The framework demonstrated that quantum-enhanced local training can coexist with end-to-end FHE encryption without prohibitive performance penalties.
• The open-source implementation is available at https://github.com/elucidator8918/QFL-MLNCP-NeurIPS, enabling reproducibility and further benchmarking.