FedMinds: Privacy-Preserving Personalized Brain Visual Decoding

TL;DR

FedMinds is a federated learning framework for privacy-preserving personalized brain visual decoding from fMRI data, achieving high accuracy while protecting individual privacy.

Contribution

This paper introduces FedMinds, a novel federated learning approach with personalized adapters for privacy-preserving brain visual decoding.

Findings

High-precision visual decoding achieved

Effective privacy protection demonstrated

Personalized models outperform generic ones

Abstract

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.