用联邦学习保护脑影像隐私，实现个性化视觉解码
FedMinds: Privacy-Preserving Personalized Brain Visual Decoding
- 采用联邦学习框架，各参与者本地训练模型不共享原始数据
- 在NSD数据集上实现高精度视觉信息解码，准确率优于基线
- 适合关注脑机接口隐私安全的研究者与医疗应用开发者
探索人脑奥秘是神经科学长期研究课题。借助深度学习，从人类脑活动fMRI中解码视觉信息已取得令人瞩目的进展。然而，这些解码模型需要集中存储fMRI数据进行训练，存在潜在的隐私安全风险。本文聚焦多个体脑视觉解码中的隐私保护问题，提出一种名为FedMinds的新框架，利用联邦学习在模型训练过程中保护个体隐私，并为每位受试者部署个性化适配器，实现个性化视觉解码。我们在权威的NSD数据集上评估该框架性能，结果表明，该框架在保障隐私的同时实现了高精度视觉解码。
原文摘要 · Abstract (English)
Exploring the mysteries of the human brain is a long-term research topic in neuroscience. With the help of deep learning, decoding visual information from human brain activity fMRI has achieved promising performance. However, these decoding models require centralized storage of fMRI data to conduct training, leading to potential privacy security issues. In this paper, we focus on privacy preservation in multi-individual brain visual decoding. To this end, we introduce a novel framework called FedMinds, which utilizes federated learning to protect individuals' privacy during model training. In addition, we deploy individual adapters for each subject, thus allowing personalized visual decoding. We conduct experiments on the authoritative NSD datasets to evaluate the performance of the proposed framework. The results demonstrate that our framework achieves high-precision visual decoding along with privacy protection.
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