Quantum federated learning (QFL) incorporates the principles of quantum neural networks (QNN) over federated learning (FL). It updates quantum parameters to the server. However, QFL aggregation involves quantum parameters from all local QNN models, leading to performance degradation due to outlier models. Thus, a novel fair QFL algorithm is proposed to enhance learning performance by eliminating outliers using Lyapunov optimization at the QFL server. This approach is used to take care of the trade-off between accuracy and latency, where the accuracy is associated with the freshness of aggregation. Moreover, a fidelity/entropy-adaptive algorithm is proposed, which relies on the degree control to select outliers. This approach (i) quantifies the dissimilarity in quantum states between the target model and each local device using fidelity, and (ii) evaluates the imbalance of data classes in each local device using entropy. Experimental results demonstrate that the proposed fair QFL algorithm outperforms benchmarks.
Quantum federated learning (QFL) incorporates the principles of quantum neural networks (QNN) over federated learning (FL). It updates quantum parameters to the server. However, QFL aggregation involves quantum parameters from all local QNN models, leading to performance degradation due to outlier models. Thus, a novel fair QFL algorithm is proposed to enhance learning performance by eliminating outliers using Lyapunov optimization at the QFL server. This approach is used to take care of the trade-off between accuracy and latency, where the accuracy is associated with the freshness of aggregation. Moreover, a fidelity/entropy-adaptive algorithm is proposed, which relies on the degree control to select outliers. This approach (i) quantifies the dissimilarity in quantum states between the target model and each local device using fidelity, and (ii) evaluates the imbalance of data classes in each local device using entropy. Experimental results demonstrate that the proposed fair QFL algorithm outperforms benchmarks.
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.
Abstract—Quantum federated learning (QFL) enables clients to train quantum neural network (QNN) models without sharing private data. We find that aggregation in QFL is unstable under heterogeneous data, unreliable communication, variable fidelity, latency, and quantum hardware noise. Moreover, QFL is non-trivial to address because several QNN parameters are periodic angles, and Euclidean averaging often fails to capture the inherent dynamics. We develop a novel self-consistent midpoint aggregation method for the stable design and implementation of QFLs. We combine QoS-aware client weighting, circular parameter aggregation, and bounded midpoint-based update control. We perform several angular tests and experiments on IBM's real Quantum machines for validation, confirming our approach. Extensive evaluations and experiments on medical and financial datasets show improved stability, lower volatility, and competitive accuracy. Index Terms—Quantum federated learning, adaptive aggregation, geometry-aware aggregation, self-consistent midpoint, quality of service, angular aggregation, quantum neural networks.
Recent advancements in Quantum Neural Networks (QNNs) have demonstrated theoretical and experimental performance superior to their classical counterparts in a wide range of applications. However, existing centralized QNNs cannot solve many real-world problems because collecting large amounts of training data to a common public site is time-consuming and, more importantly, violates data privacy. Federated Learning (FL) is an emerging distributed machine learning framework that allows collaborative model training on decentralized data residing on multiple devices without breaching data privacy. Some initial attempts at Quantum Federated Learning (QFL) either only focus on improving the QFL performance or rely on a trusted quantum server that fails to preserve data privacy. In this work, we propose CryptoQFL, a QFL framework that allows distributed QNN training on encrypted data. CryptoQFL is (1) secure, because it allows each edge to train a QNN with local private data, and encrypt its updates using quantum homomorphic encryption before sending them to the central quantum server; (2) communication-efficient, as CryptoQFL quantize local gradient updates to ternary values, and only communicate non-zero values to the server for aggregation; and (3) computation-efficient, as CryptoQFL presents an efficient quantum aggregation circuit with significantly reduced latency compared to state-of-the-art approaches.
Federated learning (FL) is a privacy-preserving alternative to centralized machine learning, where model training is performed on local devices and only global model updates are shared, effectively addressing challenges, such as data silos and privacy protection. Recently, quantum FL (QFL), an emerging FL branch, has garnered significant attention in many industry applications. However, existing QFL algorithms predominantly employ average weighting for global model training, which shows poor performance on heterogeneous healthcare data. To address this challenge, this study proposes a dynamic aggregation QFL algorithm (DAQFL) for intelligent diagnosis. Specifically, it utilizes quantum neural networks (QNNs) as local training models and designs corresponding variational quantum circuits (VQC). To mitigate performance degradation caused by the heterogeneity of medical industrial data, a dynamic aggregation method based on accuracy is proposed to enhance global model performance effectively. Extensive experiments with three distribution settings, including independent and identically distributed (IID), non-independent and identically distributed (Non-IID), and long-tail datasets, show that DAQFL outperforms baseline algorithms in accuracy and training speed. It also performs well in privacy protection and robustness of anti-noise, improving its suitability for real-world medical applications.
In recent years, the growing demand for secure, efficient, and scalable machine learning has highlighted significant challenges in traditional Federated Learning (FL) systems. These challenges include computational inefficiencies in handling large-scale, high-dimensional data, privacy concerns, and the limitations of classical hardware in solving complex optimization problems. Additionally, the increasing complexity of data and the need for more robust privacy-preserving methods have pushed the boundaries of classical FL. To address these issues, this paper presents a detailed framework for Quantum Federated Learning (QFL), where quantum computing and FL converge to enable distributed quantum model training with enhanced privacy preservation. The system is modeled using local quantum models on clients and a global aggregation strategy at the server. We develop quantum gradient-based optimization with quantum cross-entropy loss, privacy preservation through encryption, and robustness to quantum noise. A set of theorems is provided to prove the correctness and efficiency of the model, supported by rigorous mathematical formulations. We used IBM Qiskit for the simulation and compared our proposed QFL with classical FL. The results demonstrate that our QFL outperformed classical FL in terms of accuracy, precision, recall, and F1 score.
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.