mlsanityData to 7 Oct 2026

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

arXiv 2507.08217 ↗First posted 10 Jul 2025cs.AIcited by 8

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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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 26 of its 56 references that are in the map. First posted on arXiv 10 Jul 2025.

Builds on

26 of 56 references.

References · 26

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  5. 2020Variational Quantum Algorithmscited by 687
  6. 2018Supervised learning with quantum enhanced feature spacescited by 654
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  8. 2020Power of data in quantum machine learningcited by 336
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  10. 2016Quantum Machine Learningcited by 325
  11. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  12. 2021SMIL: Multimodal Learning with Severely Missing Modalitycited by 243
14 more
  1. 2023Multi-modal Learning with Missing Modality via Shared-Specific Feature Modellingcited by 159
  2. 2018Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalitiescited by 149
  3. 2023FedMultimodal: A Benchmark For Multimodal Federated Learningcited by 51
  4. 2022Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learningcited by 45
  5. 2023Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptationcited by 27
  6. 2023Foundations of Quantum Federated Learning Over Classical and Quantum Networkscited by 21
  7. 2015EmoNets: Multimodal deep learning approaches for emotion recognition in videocited by 20
  8. 2024FedQNN: Federated Learning using Quantum Neural Networkscited by 20
  9. 2018Deep learning for affective computing: text-based emotion recognition in decision supportcited by 14
  10. 2024Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approachcited by 11
  11. 2025Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learningcited by 7
  12. 2024False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learningcited by 5
  13. 2024Electrocardiogram (ECG) Based Cardiac Arrhythmia Detection and Classification using Machine Learning Algorithmscited by 4
  14. 2024Performance Evaluation of LoRa Technology for Rural Connectivity: An Experimental Analysis in Nepalcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.

  1. 2025Quantum Federated Learning: A Comprehensive Surveycited by 7
  2. 2025Towards Personalized Quantum Federated Learning for Anomaly Detectioncited by 4
  3. 2025SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualizationcited by 2
  4. 2025Differentially Private Federated Quantum Learning via Quantum Noisecited by 1
  5. 2025Expressive and Scalable Quantum Fusion for Multimodal Learningcited by 1
  6. 2026Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approachcited by 0
  7. 2026Boosting Multimodal Federated Learning via Chained Modality Optimizationcited by 0
  8. 2026MQAdapter: Multi-Modal Quantum Adapter for Coarse-to-Fine VLM Fine-tuningcited by 0