Metadata-Driven Federated Learning of Connectional Brain Templates in
  Non-IID Multi-Domain Scenarios
Geng Chen Qingyue Wang Islem Rekik
A connectional brain template (CBT) is a holistic representation of a
population of multi-view brain connectivity graphs, encoding shared patterns
and normalizing typical variations across individuals. The federation of CBT
learning allows for an inclusive estimation of the representative center of
multi-domain brain connectivity datasets in a fully data-preserving manner.
However, existing methods overlook the non-independent and identically
distributed (non-IDD) issue stemming from multidomain brain connectivity
heterogeneity, in which data domains are drawn from different hospitals and
imaging modalities. To overcome this limitation, we unprecedentedly propose a
metadata-driven federated learning framework, called MetaFedCBT, for
cross-domain CBT learning. Given the data drawn from a specific domain (i.e.,
hospital), our model aims to learn metadata in a fully supervised manner by
introducing a local client-based regressor network. The generated meta-data is
forced to meet the statistical attributes (e.g., mean) of other domains, while
preserving their privacy. Our supervised meta-data generation approach boosts
the unsupervised learning of a more centered, representative, and holistic CBT
of a particular brain state across diverse domains. As the federated learning
progresses over multiple rounds, the learned metadata and associated generated
connectivities are continuously updated to better approximate the target domain
information. MetaFedCBT overcomes the non-IID issue of existing methods by
generating informative brain connectivities for privacy-preserving holistic CBT
learning with guidance using metadata. 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 the state-of-the-art performance.