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International Conference on Quantum Communications, Networking, and Computing (QCNC)

A2G-QFL: Adaptive Aggregation with Two Gains in Quantum Federated Learning

Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel

Published Apr 6, 2026• 0 citations
Publisher

Abstract

Federated learning (FL) deployed over quantumenabled and heterogeneous classical networks can degrade under uneven client quality, modeled teleportation/link fidelity, and communication instability, while standard aggregation rules assume Euclidean topology and uniform reliability, limiting their suitability for emerging quantum federated systems. We introduce A2G (Adaptive Aggregation with Two Gains). This dualgain framework jointly regulates server-side relaxation through a geometry gain. It modulates client importance using a QoS gain derived from teleportation/link fidelity, latency, and instability (variance of the QoS signal). The proposed A2G update rule shows that A2G recovers baseline approaches such as FedAvg, Latency-only, QoS-aware averaging, and manifold-inspired aggregation as special cases. Experiments on a Qiskit Aer-based quantum-classical simulation testbed demonstrate improved stability and accuracy under heterogeneous, realistic, and simulated quantum-noisy conditions.

DOI
10.1109/qcnc69040.2026.00132

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Federated learning (FL) deployed over quantumenabled and heterogeneous classical networks can degrade under uneven client quality, modeled teleportation/link fidelity, and communication instability, while standard aggregation rules assume Euclidean topology and uniform reliability, limiting their suitability for emerging quantum federated systems. We introduce A2G (Adaptive Aggregation with Two Gains). This dualgain framework jointly regulates server-side relaxation through a geometry gain. It modulates client importance using a QoS gain derived from teleportation/link fidelity, latency, and instability (variance of the QoS signal). The proposed A2G update rule shows that A2G recovers baseline approaches such as FedAvg, Latency-only, QoS-aware averaging, and manifold-inspired aggregation as special cases. Experiments on a Qiskit Aer-based quantum-classical simulation testbed demonstrate improved stability and accuracy under heterogeneous, realistic, and simulated quantum-noisy conditions.
Federated learning (FL) deployed over quantum enabled and heterogeneous classical networks faces significant performance degradation due to uneven client quality, stochastic teleportation fidelity, device instability, and geometric mismatch between local and global models. Classical aggregation rules assume euclidean topology and uniform communication reliability, limiting their suitability for emerging quantum federated systems. This paper introduces A2G (Adaptive Aggregation with Two Gains), a dual gain framework that jointly regulates geometric blending through a geometry gain and modulates client importance using a QoS gain derived from teleportation fidelity, latency, and instability.
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Quantum Federated Learning (QFL) offers a promising framework to train quantum models across distributed clients while keeping data strictly local. Due to its simplicity and low communication overhead, Federated Averaging (FedAvg) is the standard aggregation choice in QFL literature. However, deploying QFL on practical hardware exposes a severe double-drift phenomenon: the global model is simultaneously derailed by client drift from non-IID data and hardware bias from noisy quantum gradient estimates. In this work, we first analyze the convergence of FedAvg under these realistic conditions, mathematically demonstrating that quantum hardware bias creates a persistent error floor that standard averaging cannot correct. To overcome this limitation, we propose Q-ANCHOR, a quantum-aware federated aggregation architecture that anchors server updates with zero-noise extrapolation while applying stateful client correction to suppress both client drift and hardware-induced bias. Our convergence theory proves that Q-ANCHOR successfully mitigates classical client drift while actively reducing the hardware-bias floor. Experimental results demonstrate that Q-ANCHOR achieves significantly more stable training than conventional FL baselines.
Quantum federated learning (QFL) incorporates the principles of quantum neural networks (QNNs) and federated learning (FL), ensuring privacy by updating quantum parameters to the server. However, server aggregation in general QFL involves quantum parameters from all local models, leading to performance degradation due to the participation of outlier heterogeneous local models. Inspired by this, a novel QFL algorithm is proposed to enhance learning performance by excluding outliers before aggregation using Lyapunov optimization at QFL server. This Lyapunov optimization is used to take care of the tradeoff between accuracy and latency, where the accuracy is associated with the freshness of aggregation. Additionally, an entropy-and-fidelity aware algorithm is proposed, which relies on the degree control to select outliers for exclusion comprehensively. This algorithm addresses data and quantum aspects by evaluating the imbalance of data classes in each local model using entropy and quantifying the dissimilarity in quantum states between the target model and each local model through fidelity. Experimental results demonstrate that the proposed algorithm outperforms benchmarks, effectively excluding heterogeneous local models to improve performance while ensuring stability.
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We propose FAAA−β\text{FAAA}-\betaFAAA−β (Fidelity-Aware Angular Aggregation), a novel teleportation-conditioned aggregation framework for quantum learning (QFL) that incorporates quantum channel quality metrics into the model update process; β\betaβ is a tunable blending parameter. FAAA−β\text{FAAA}-\betaFAAA−β harnesses variational parameters as angular coordinates on a torus and performs fidelity-weighted circular consensus with parameterized linear blending. Our method computes trust weights based on measured fidelity FiF_{i}Fi​, latency τi\tau_{i}τi​, and instability σi2\sigma_{i}^{2}σi2​, establishing a crosslayer coupling between the physical-layer teleportation quality and the model-layer aggregation. Experimental evaluation using SamplerQNN on binary classification tasks under synthetically degraded Bell-pair fidelities demonstrates that FAAA−β\text{FAAA}-\betaFAAA−β achieves competitive accuracy with significantly reduced variance and enhanced stability.