用量子计算缓解加密联邦学习的性能下降，提升多模态数据建模能力。
MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption
- 将量子混合专家模型与全同态加密结合，提升隐私保护下的多模态学习效果。
- 在基因组与脑影像数据上，对少数类别识别准确率显著提升。
- 首次实现量子增强联邦学习框架，适合隐私敏感的医疗多模态应用。
在联邦学习中引入全同态加密（FHE）虽大幅提升了数据隐私保护水平，但在聚合阶段常导致模型性能下降，制约了表征泛化能力的提升。本文提出一种新型多模态量子联邦学习框架（MQFL-FHE），利用量子计算抵消FHE带来的性能损耗。首次在联邦学习中结合多模态量子混合专家（MQMoE）模型与FHE，融合多模态数据以增强表示能力和任务特定学习。实验表明，该框架在多模态数据及基因组与脑磁共振成像（MRI）联合数据集上表现优异，尤其提升了低频类别的分类准确率。结果验证了量子干预在保障隐私的同时可有效缓解FHE导致的性能退化，展现出量子技术赋能安全联邦学习的巨大潜力。
原文摘要 · Abstract (English)
The integration of fully homomorphic encryption (FHE) in federated learning (FL) has led to significant advances in data privacy. However, during the aggregation phase, it often results in performance degradation of the aggregated model, hindering the development of robust representational generalization. In this work, we propose a novel multimodal quantum federated learning framework that utilizes quantum computing to counteract the performance drop resulting from FHE. For the first time in FL, our framework combines a multimodal quantum mixture of experts (MQMoE) model with FHE, incorporating multimodal datasets for enriched representation and task-specific learning. Our MQMoE framework enhances performance on multimodal datasets and combined genomics and brain MRI scans, especially for underrepresented categories. Our results also demonstrate that the quantum-enhanced approach mitigates the performance degradation associated with FHE and improves classification accuracy across diverse datasets, validating the potential of quantum interventions in enhancing privacy in FL.
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