mlsanityData to 13 Oct 2026

A Stable Aggregation Method for Quantum Federated Learning

arXiv 2609.00356 ↗First posted 25 Jun 2026cs.AI cs.LGcited by 0

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Abstract (arXiv)

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-trivially challenging because several QNN parameters are periodic angles, where Euclidean averaging often fails to capture the inherent dynamics. We develop a novel self-consistent midpoint aggregation method for stable QFL design and implementation. We combine QoS-aware client weighting, circular parameter aggregation, and bounded midpoint-based update control. We perform several angular tests and IBM real Quantum machines experiments for validation confirming our approach. Extensive evaluations and experiments on medical and financial datasets show improved stability, lower volatility, and competitive accuracy.

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References · 13

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,653
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,981
  3. 2021Federated Quantum Machine Learningcited by 82
  4. 2021Quantum Federated Learning with Quantum Datacited by 53
  5. 2022Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluationcited by 40
  6. 2021QuantumFed: A Federated Learning Framework for Collaborative Quantum Trainingcited by 22
  7. 2023Foundations of Quantum Federated Learning Over Classical and Quantum Networkscited by 21
  8. 2022Federated Learning on Riemannian Manifoldscited by 13
  9. 2024Massive Digital Over-the-Air Computation for Communication-Efficient Federated Edge Learningcited by 13
  10. 2023Federated Learning with Manifold Regularization and Normalized Update Reaggregationcited by 8
  11. 2024Riemannian Federated Learning via Averaging Gradient Streamscited by 4
  12. 2026FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnetcited by 2
1 more
  1. 2026Fedcompass: Federated Clustered and Periodic Aggregation Framework for Hybrid Classical-Quantum Modelscited by 1

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