# Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach

arXiv: https://arxiv.org/abs/2507.08217 · HTML: https://mlsanity.com/paper/2507.08217/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 56 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [SMIL: Multimodal Learning with Severely Missing Modality](https://mlsanity.com/paper/2103.05677/) (2021)
- [Multi-modal Learning with Missing Modality via Shared-Specific Feature Modelling](https://mlsanity.com/paper/2307.14126/) (2023)
- [Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities](https://mlsanity.com/paper/1812.07809/) (2018)
- [FedMultimodal: A Benchmark For Multimodal Federated Learning](https://mlsanity.com/paper/2306.09486/) (2023)
- [Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning](https://mlsanity.com/paper/2210.00226/) (2022)
- [Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation](https://mlsanity.com/paper/2310.03986/) (2023)
- [Foundations of Quantum Federated Learning Over Classical and Quantum Networks](https://mlsanity.com/paper/2310.14516/) (2023)
- [EmoNets: Multimodal deep learning approaches for emotion recognition in video](https://mlsanity.com/paper/1503.01800/) (2015)
- [FedQNN: Federated Learning using Quantum Neural Networks](https://mlsanity.com/paper/2403.10861/) (2024)
- [Deep learning for affective computing: text-based emotion recognition in decision support](https://mlsanity.com/paper/1803.06397/) (2018)
- [Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach](https://mlsanity.com/paper/2411.10619/) (2024)
- [Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning](https://mlsanity.com/paper/2502.17226/) (2025)
- [False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning](https://mlsanity.com/paper/2411.01313/) (2024)
- [Electrocardiogram (ECG) Based Cardiac Arrhythmia Detection and Classification using Machine Learning Algorithms](https://mlsanity.com/paper/2412.05583/) (2024)
- [Performance Evaluation of LoRa Technology for Rural Connectivity: An Experimental Analysis in Nepal](https://mlsanity.com/paper/2412.04563/) (2024)

## Built on by (most cited first, 8 of 8)

- [Quantum Federated Learning: A Comprehensive Survey](https://mlsanity.com/paper/2508.15998/) (2025)
- [Towards Personalized Quantum Federated Learning for Anomaly Detection](https://mlsanity.com/paper/2511.07471/) (2025)
- [SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization](https://mlsanity.com/paper/2508.12477/) (2025)
- [Differentially Private Federated Quantum Learning via Quantum Noise](https://mlsanity.com/paper/2508.20310/) (2025)
- [Expressive and Scalable Quantum Fusion for Multimodal Learning](https://mlsanity.com/paper/2510.06938/) (2025)
- [Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approach](https://mlsanity.com/paper/2601.07882/) (2026)
- [Boosting Multimodal Federated Learning via Chained Modality Optimization](https://mlsanity.com/paper/2606.01856/) (2026)
- [MQAdapter: Multi-Modal Quantum Adapter for Coarse-to-Fine VLM Fine-tuning](https://mlsanity.com/paper/2607.12418/) (2026)

## Abstract (arXiv)

Quantum federated learning (QFL) has been recently introduced to enable a distributed privacy-preserving quantum machine learning (QML) model training across quantum processors (clients). Despite recent research efforts, existing QFL frameworks predominantly focus on unimodal systems, limiting their applicability to real-world tasks that often naturally involve multiple modalities. To fill this significant gap, we present for the first time a novel multimodal approach specifically tailored for the QFL setting with the intermediate fusion using quantum entanglement. Furthermore, to address a major bottleneck in multimodal QFL, where the absence of certain modalities during training can degrade model performance, we introduce a Missing Modality Agnostic (MMA) mechanism that isolates untrained quantum circuits, ensuring stable training without corrupted states. Simulation results demonstrate that the proposed multimodal QFL method with MMA yields an improvement in accuracy of 6.84% in independent and identically distributed (IID) and 7.25% in non-IID data distributions compared to the state-of-the-art methods.