Graph augmentation guided federated knowledge distillation for multisite functional MRI analysis

Abstract

Resting-state functional MRI (rs-fMRI) is a non-invasive tool increasingly used to detect abnormalities in brain connectivity for disorder analysis. Many learning models have been explored for fMRI analysis but usually require extensive data for reliable training. Multisite studies increase sample sizes by pooling data from multiple sites, but often face data security and privacy challenges. Federated learning (FL) facilitates collaborative model training without pooling fMRI data from different sites/clients. However, many FL methods share model parameters between clients, posing significant security risks during communication and greatly increasing communication costs. Besides, fMRI data for local model training is usually limited at each site, which may hinder local model training. To this end, we propose a graph augmentation guided federated distillation (GAFD) framework for multisite fMRI analysis and brain disorder identification. At each client, we augment each input functional connectivity network/graph derived from fMRI by perturbing node features and edges, followed by a feature encoder for graph representation learning. A contrastive loss is used to maximize the agreement of learned representations from the same subject, further enhancing discriminative power of fMRI representations. On the server side, the server receives model outputs (i.e., logit scores) corresponding to augmented graphs from each client and merges them. The merged logit score is then sent back to each client for knowledge distillation. This can promote knowledge sharing among clients, reduce the risk of privacy leakage, and greatly decrease communication costs. Experimental results on two multisite fMRI datasets indicate that our approach outperforms several state-of-the-arts.