Peer-to-Peer Generative Learning for Architecture-Agnostic Federated MRI Reconstruction

Motivation: Federated learning (FL) enables privacy-preserving training of deep reconstruction models across multiple sites to improve generalization at the expense of lower within-site performance. Yet, existing methods require a common model architecture across sites, limiting flexibility. Goal(s): Our goal was to devise an architecture-agnostic method for collaborative training of heterogeneous models across sites. Approach: We introduced a novel peer-to-peer generative learning method (PGL-FedMR), where individual sites share a generative prior for their MRI data with remaining sites, and prior-driven synthetic data are used to train reconstruction models at each site. Results: PGL-FedMR improves across-site generalization over local models, and within-site performance over conventional FL. Impact: Improvements in within-site and across-site performance for MRI reconstruction through PGL-FedMR, coupled with the ability to handle heterogeneous architectures, may facilitate privacy-preserving multi-institutional collaborations to build reliable reconstruction models for many applications where data are scarce including rare diseases.