AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing
Journal:
arXiv
Published Date:
Jul 9, 2025
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.
