Citation

Jahin, Md Abrar and Fuad, Taufikur Rahman and Mridha, M. F. and Fahad, Nafiz and Hossen, Md. Jakir (2025) AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing. In: 2025 IEEE/CVF International Conference on Computer Vision Workshops, ICCV-W 2025, 19 October 2025 - 20 October 2025, Honolulu.

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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 quantumenhanced 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 CIFAR10, SVHN, and Fashion-MNIST datasets. AdeptHEQ-FL achieves a ≈ 25.43% and ≈ 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. Our code is publicly available at: https://github.com/Abrar2652/QML-FL.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Federated learning, machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	20 Apr 2026 02:38
Last Modified:	20 Apr 2026 02:38
URII:	http://shdl.mmu.edu.my/id/eprint/15756

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