FedMinds: Privacy-Preserving Personalized Brain Visual Decoding
This addresses privacy concerns for neuroscience researchers using fMRI data, though it is incremental as it applies existing federated learning to a specific domain.
The paper tackles privacy issues in multi-individual brain visual decoding from fMRI data by introducing FedMinds, a federated learning framework with personalized adapters, achieving high-precision decoding on the NSD dataset.
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.