  Predicting hospital length of stay (LOS) reliably is an essential need for
efficient resource allocation at hospitals. Traditional predictive modeling
tools frequently have difficulty acquiring sufficient and diverse data because
healthcare institutions have privacy rules in place. In our study, we modeled
this problem as an empirical graph where nodes are the hospitals. This modeling
approach facilitates collaborative model training by modeling decentralized
data sources from different hospitals without extracting sensitive data outside
of hospitals. A local model is trained on a node (hospital) by aiming the
generalized total variation minimization (GTVMin). Moreover, we implemented and
compared two different federated learning optimization algorithms named
federated stochastic gradient descent (FedSGD) and federated averaging
(FedAVG). Our results show that federated learning enables accurate prediction
of hospital LOS while addressing privacy concerns without extracting data
outside healthcare institutions.
