Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios.

Abstract

A connectional brain template (CBT) is a holistic representation of a population of brain connectivities. The federated learning of CBT allows for estimating the CBT of brain connectivities from multiple domains (i.e., hospitals) in a fully data-preserving manner. However, existing methods overlook the non-independent and identically distributed (non-IID) issue stemming from the heterogeneity of multi-domain brain connectivities. This non-IID issue degrades the centrality of locally learned CBT from multiple decentralized domains, eventually leading to a limited representation ability. To overcome this limitation, we propose a metadata-driven federated learning framework, called MetaFedCBT, for multi-domain CBT learning under the non-IID condition. Given the data drawn from a specific domain, our model is able to predict the metadata (i.e., statistics) of other unseen domains with a proposed metadata regressor and local-global network residual weights. Furthermore, we introduce a metadata-driven connectivity generator to predict brain connectivities of unseen domains under the guidance of obtained metadata. As the federated learning progresses over multiple rounds, we continuously update the predicted metadata and brain connectivities to better approximate the unseen domains. MetaFedCBT overcomes the non-IID issue by generating informative brain connectivities for privacy-preserving holistic CBT learning. Extensive experiments on multi-view morphological brain networks of normal and patient subjects demonstrate that our MetaFedCBT is a superior federated CBT learning model and significantly advances state-of-the-art performance.