mlsanityData to 14 Oct 2026

Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise

arXiv 2507.12492 ↗First posted 15 Jul 2025quant-ph cs.AI cs.LGcited by 8

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Abstract (arXiv)

Quantum Federated Learning (QFL) is an emerging paradigm that combines quantum computing and federated learning (FL) to enable decentralized model training while maintaining data privacy over quantum networks. However, quantum noise remains a significant barrier in QFL, since modern quantum devices experience heterogeneous noise levels due to variances in hardware quality and sensitivity to quantum decoherence, resulting in inadequate training performance. To address this issue, we propose SpoQFL, a novel QFL framework that leverages sporadic learning to mitigate quantum noise heterogeneity in distributed quantum systems. SpoQFL dynamically adjusts training strategies based on noise fluctuations, enhancing model robustness, convergence stability, and overall learning efficiency. Extensive experiments on real-world datasets demonstrate that SpoQFL significantly outperforms conventional QFL approaches, achieving superior training performance and more stable convergence.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 19 of its 37 references that are in the map. First posted on arXiv 15 Jul 2025.

Builds on

19 of 37 references.

References · 19

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  4. 2021Model-Contrastive Federated Learningcited by 766
  5. 2020Power of data in quantum machine learningcited by 338
  6. 2016Quantum Machine Learningcited by 326
  7. 2021Quantum Federated Learning with Quantum Datacited by 53
  8. 2022On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyondcited by 38
  9. 2021QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalizationcited by 27
  10. 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
  11. 2024Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approachcited by 11
  12. 2024MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryptioncited by 7
7 more
  1. 2025Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learningcited by 7
  2. 2022Error Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysiscited by 6
  3. 2024False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learningcited by 5
  4. 2024Personalized Quantum Federated Learning for Privacy Image Classificationcited by 4
  5. 2024Electrocardiogram (ECG) Based Cardiac Arrhythmia Detection and Classification using Machine Learning Algorithmscited by 4
  6. 2024Performance Evaluation of LoRa Technology for Rural Connectivity: An Experimental Analysis in Nepalcited by 2
  7. 2024Microcontroller-Driven MPPT System for Enhanced Photovoltaic Efficiency: An Experimental Approach in Nepalcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 1.

8 papers.

  1. 2025Quantum Federated Learning: A Comprehensive Surveycited by 10
  2. 2025Layerwise Federated Learning for Heterogeneous Quantum Clients using Quoruscited by 5
  3. 2025Towards Personalized Quantum Federated Learning for Anomaly Detectioncited by 4
  4. 2025SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualizationcited by 2
  5. 2025Differentially Private Federated Quantum Learning via Quantum Noisecited by 2
  6. 2025Escaping Barren Plateaus in Variational Quantum Algorithms Using Negative Learning Rate in Quantum Internet of Thingscited by 1
  7. 2025Towards Heterogeneous Quantum Federated Learning: Challenges and Solutionscited by 0
  8. 2026Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approachcited by 0