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Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approach

Ratun Rahman, Shaba Shaon, Dinh C. Nguyen

TL;DR

This work tackles heterogeneity in quantum federated learning by introducing SPQFL, which integrates sporadic learning to mitigate quantum noise and personalized learning to address non-IID quantum data. The authors provide a convergence analysis under standard assumptions and validate the approach through simulations on MNIST, FashionMNIST, CIFAR-100, and Caltech-101, showing improvements over state-of-the-art methods, including up to 6.25% accuracy gains. The SPQFL framework relies on decentralized noise-aware updates and regularization-based personalization to stabilize global convergence in heterogeneous quantum networks. The results demonstrate SPQFL's robustness to noise and data heterogeneity, suggesting practical benefits for scalable quantum learning on NISQ devices.

Abstract

Quantum federated learning (QFL) emerges as a powerful technique that combines quantum computing with federated learning to efficiently process complex data across distributed quantum devices while ensuring data privacy in quantum networks. Despite recent research efforts, existing QFL frameworks struggle to achieve optimal model training performance primarily due to inherent heterogeneity in terms of (i) quantum noise where current quantum devices are subject to varying levels of noise due to varying device quality and susceptibility to quantum decoherence, and (ii) heterogeneous data distributions where data across participating quantum devices are naturally non-independent and identically distributed (non-IID). To address these challenges, we propose a novel integrated sporadic-personalized approach called SPQFL that simultaneously handles quantum noise and data heterogeneity in a single QFL framework. It is featured in two key aspects: (i) for quantum noise heterogeneity, we introduce a notion of sporadic learning to tackle quantum noise heterogeneity across quantum devices, and (ii) for quantum data heterogeneity, we implement personalized learning through model regularization to mitigate overfitting during local training on non-IID quantum data distributions, thereby enhancing the convergence of the global model. Moreover, we conduct a rigorous convergence analysis for the proposed SPQFL framework, with both sporadic and personalized learning considerations. Theoretical findings reveal that the upper bound of the SPQFL algorithm is strongly influenced by both the number of quantum devices and the number of quantum noise measurements. Extensive simulation results in real-world datasets also illustrate that the proposed SPQFL approach yields significant improvements in terms of training performance and convergence stability compared to the state-of-the-art methods.

Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approach

TL;DR

This work tackles heterogeneity in quantum federated learning by introducing SPQFL, which integrates sporadic learning to mitigate quantum noise and personalized learning to address non-IID quantum data. The authors provide a convergence analysis under standard assumptions and validate the approach through simulations on MNIST, FashionMNIST, CIFAR-100, and Caltech-101, showing improvements over state-of-the-art methods, including up to 6.25% accuracy gains. The SPQFL framework relies on decentralized noise-aware updates and regularization-based personalization to stabilize global convergence in heterogeneous quantum networks. The results demonstrate SPQFL's robustness to noise and data heterogeneity, suggesting practical benefits for scalable quantum learning on NISQ devices.

Abstract

Quantum federated learning (QFL) emerges as a powerful technique that combines quantum computing with federated learning to efficiently process complex data across distributed quantum devices while ensuring data privacy in quantum networks. Despite recent research efforts, existing QFL frameworks struggle to achieve optimal model training performance primarily due to inherent heterogeneity in terms of (i) quantum noise where current quantum devices are subject to varying levels of noise due to varying device quality and susceptibility to quantum decoherence, and (ii) heterogeneous data distributions where data across participating quantum devices are naturally non-independent and identically distributed (non-IID). To address these challenges, we propose a novel integrated sporadic-personalized approach called SPQFL that simultaneously handles quantum noise and data heterogeneity in a single QFL framework. It is featured in two key aspects: (i) for quantum noise heterogeneity, we introduce a notion of sporadic learning to tackle quantum noise heterogeneity across quantum devices, and (ii) for quantum data heterogeneity, we implement personalized learning through model regularization to mitigate overfitting during local training on non-IID quantum data distributions, thereby enhancing the convergence of the global model. Moreover, we conduct a rigorous convergence analysis for the proposed SPQFL framework, with both sporadic and personalized learning considerations. Theoretical findings reveal that the upper bound of the SPQFL algorithm is strongly influenced by both the number of quantum devices and the number of quantum noise measurements. Extensive simulation results in real-world datasets also illustrate that the proposed SPQFL approach yields significant improvements in terms of training performance and convergence stability compared to the state-of-the-art methods.
Paper Structure (25 sections, 4 theorems, 46 equations, 5 figures, 6 tables, 1 algorithm)

This paper contains 25 sections, 4 theorems, 46 equations, 5 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Introduction
  3. Related Works
  4. Quantum Machine Learning
  5. Quantum Federated Learning
  6. Noise aware Learning
  7. Proposed Integrated Sporadic-Personalized QFL (SPQFL) Methodology
  8. Local Model Training
  9. Effects of Quantum Noise
  10. Proposed Sporadic Learning Method
  11. Personalized Learning For Handling Quantum Data Heterogeneity
  12. Convergence Analysis
  13. Detailed Proofs
  14. Proof of Lemma \ref{['Lemma1']}
  15. Proof of Lemma \ref{['Lemma2']}
  16. ...and 10 more sections

Key Result

Lemma 5.1

Let Assumption Assump:Variance-gradient hold, the expected upper bound of the variance of the stochastic gradient on local model training is given as $\mathbb{E} ||g_k^t- \bar{g}_k^t||^2 \leq \frac{\sigma_g^2}{N^2}$, where $\sigma_g^2 = \frac{\nu N_{h} D Tr(H^{2})}{2M}$.

Figures (5)

  • Figure 1: Proposed SPQFL architecture where a set of distributed quantum devices collaborate with a quantum server to train a shared QML model. The proposed framework encompasses two key aspects, namely sporadic learning and personalized learning. The sporadicity term captures noise heterogeneity, while the personalization term addresses data heterogeneity.
  • Figure 2: Difference in performance in quantum learning with basic QNN and QFL Non-IID with 3,5, and 10 devices.
  • Figure 3: Difference in performance in quantum learning with basic QNN, QFL, and PQFL for 10 devices.
  • Figure 4: Difference in performance between PQFL and SPQFL (PQFL with sporadic approach) for noise mitigation.
  • Figure 5: Training performance comparison of SPQFL on the MNIST dataset (non-IID), showing both accuracy and loss under different quantum measurement conditions.

Theorems & Definitions (10)

  • Lemma 5.1
  • proof
  • Lemma 5.2
  • proof
  • Lemma 5.3
  • proof
  • Theorem 5.1
  • proof
  • Remark 5.1
  • Remark 5.2