AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing

TL;DR

AdeptHEQ-FL is a novel federated learning framework combining hybrid classical-quantum models, adaptive aggregation, selective homomorphic encryption, and dynamic layer freezing to enhance privacy, efficiency, and expressivity.

Contribution

It introduces a unified hybrid classical-quantum FL framework with adaptive privacy-preserving techniques and dynamic layer management, improving accuracy and reducing communication overhead.

Findings

Achieves approximately 25.43% accuracy improvement on CIFAR-10 over Standard-FedQNN.

Reduces communication overhead by freezing less important layers.

Provides formal privacy guarantees and convergence analysis.

Abstract

Federated Learning (FL) faces inherent challenges in balancing model performance, privacy preservation, and communication efficiency, especially in non-IID decentralized environments. Recent approaches either sacrifice formal privacy guarantees, incur high overheads, or overlook quantum-enhanced expressivity. We introduce AdeptHEQ-FL, a unified hybrid classical-quantum FL framework that integrates (i) a hybrid CNN-PQC architecture for expressive decentralized learning, (ii) an adaptive accuracy-weighted aggregation scheme leveraging differentially private validation accuracies, (iii) selective homomorphic encryption (HE) for secure aggregation of sensitive model layers, and (iv) dynamic layer-wise adaptive freezing to minimize communication overhead while preserving quantum adaptability. We establish formal privacy guarantees, provide convergence analysis, and conduct extensive experiments on the CIFAR-10, SVHN, and Fashion-MNIST datasets. AdeptHEQ-FL achieves a $\approx 25.43\%$ and $\approx 14.17\%$ accuracy improvement over Standard-FedQNN and FHE-FedQNN, respectively, on the CIFAR-10 dataset. Additionally, it reduces communication overhead by freezing less important layers, demonstrating the efficiency and practicality of our privacy-preserving, resource-aware design for FL.