Fast and Privacy-Preserving Federated Joint Estimator of Multi-sUGMs

Learning multiple related graphs from many distributed and privacy-required resources is an important and common task in neuroscience applications. Medical researchers can comprehensively investigate the diagnostic evidence and understand the cause of certain brain diseases via analyzing the commonalities and differences of the brain connectomes predicted from the fMRI data across multiple hospitals. Previous sparse Undirected Graphical Model (sUGM) methods either cannot take full usage of the heterogeneous data while preserving privacy or miss the capability of handling the nonparanormal data, which is highly non-independent and identically distributed (non-i.i.d.). This paper proposes a novel and efficient approach, FEDJEM (
fed
erated
j
oint
e
stimator of
m
ultiple sUGMs), that trains the multi-sUGMs over a massive network encompassing various local devices and the global center. In order to efficiently process the datasets with different nonparanormal distributions, the proposed federated algorithm fully exploits the computing power of the local devices and cloud center while federated updates ensure that personal data remain local, thus defending the privacy. We also implement a general federated learning framework for multi-task learning based on our method. We apply our method on multiple simulation datasets to evaluate its speed and accuracy in comparison with relevant baselines and develop a strategy accordingly to balance its computation and communication abilities. Finally, we predict several informative groups of connectomes based on the real-world dataset.