QFL-Adaptive: A Hybrid Approach to Personalized and Resilient Quantum Federated Learning

• Rómulo W. C. Bustincio UNICAMP http://orcid.org/0000-0002-3971-5105
• Edgar C. Pozo Universidad de Guanajuato https://orcid.org/0000-0002-4120-8495
• Ricardo J. Hancco-Ancori Universidad Nacional de San Agustín de Arequipa https://orcid.org/0000-0001-8308-4833
• Francisco Airton Silva UFPI http://orcid.org/0000-0002-8211-6060
• Allan M. de Souza UFPI https://orcid.org/0000-0002-5518-8392
• Luiz F. Bittencourt UFPI https://orcid.org/0000-0001-6305-9059

DOI: https://doi.org/10.5753/sbrc.2026.19918

Abstract

Quantum Federated Learning (QFL) emerges as a paradigm for distributed training with privacy preservation. However, its practical implementation in the Noisy Intermediate-Scale Quantum (NISQ) era faces critical barriers: communication overhead and model drift caused by Non-Independent and Identically Distributed (Non-IID) data. This work proposes QFL-Adaptive, a framework that integrates a slimmable VQC-inspired architecture with personalized aggregation via Weighted Personalized QFL (wp-QFL). The architecture follows a Variational Quantum Circuit (VQC)-inspired pole-angle segmentation into Angle (ϕ) and Pole (θ) parameters, allowing for dynamic adjustment of the transmission load through conditional uplink based on channel telemetry. Results across four datasets show 77.81% bandwidth saving and over 97.9% accuracy retention under 80% communication failure. Real hardware validation remains essential to confirm efficacy against systemic decoherence.

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