首个面向多模态数据的量子联邦学习方法，解决缺失模态问题。
Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach
- 利用量子纠缠实现跨模态中间融合，支持多模态联合训练。
- 引入缺失模态无关机制，避免因缺模态导致模型性能下降。
- 在非独立同分布数据下提升准确率7.25%，适合隐私敏感场景。
量子联邦学习（QFL）近年来被提出，用于在量子处理器（客户端）间分布式地进行隐私保护的量子机器学习（QML）模型训练。尽管已有研究进展，现有QFL框架主要针对单模态系统，限制了其在涉及多种模态的实际任务中的应用。为填补这一重要空白，我们首次提出一种专为QFL场景设计的多模态方法，通过量子纠缠实现中间融合。此外，为解决多模态QFL中因训练时缺少某些模态而导致模型性能下降的关键瓶颈，我们引入缺失模态无关（MMA）机制，隔离未训练的量子电路，确保训练过程稳定且无状态污染。仿真结果表明，所提出的多模态QFL方法结合MMA，在独立同分布（IID）数据下相比最先进方法提升准确率6.84%，在非独立同分布（non-IID）数据下提升7.25%。
原文摘要 · Abstract (English)
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.
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