# A Stable Aggregation Method for Quantum Federated Learning

arXiv: https://arxiv.org/abs/2609.00356 · HTML: https://mlsanity.com/paper/2609.00356/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Quantum Machine Learning](https://mlsanity.com/paper/2103.12010/) (2021)
- [Quantum Federated Learning with Quantum Data](https://mlsanity.com/paper/2106.00005/) (2021)
- [Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation](https://mlsanity.com/paper/2211.13358/) (2022)
- [QuantumFed: A Federated Learning Framework for Collaborative Quantum Training](https://mlsanity.com/paper/2106.09109/) (2021)
- [Foundations of Quantum Federated Learning Over Classical and Quantum Networks](https://mlsanity.com/paper/2310.14516/) (2023)
- [Federated Learning on Riemannian Manifolds](https://mlsanity.com/paper/2206.05668/) (2022)
- [Massive Digital Over-the-Air Computation for Communication-Efficient Federated Edge Learning](https://mlsanity.com/paper/2405.15969/) (2024)
- [Federated Learning with Manifold Regularization and Normalized Update Reaggregation](https://mlsanity.com/paper/2311.05924/) (2023)
- [Riemannian Federated Learning via Averaging Gradient Streams](https://mlsanity.com/paper/2409.07223/) (2024)
- [FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet](https://mlsanity.com/paper/2604.22494/) (2026)
- [Fedcompass: Federated Clustered and Periodic Aggregation Framework for Hybrid Classical-Quantum Models](https://mlsanity.com/paper/2602.03052/) (2026)

## 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.