  We investigate the problem of private read update write (PRUW) with
heterogeneous storage constrained databases in federated submodel learning
(FSL). In FSL a machine learning (ML) model is divided into multiple submodels
based on different types of data used to train it. A given user downloads,
updates and uploads the updates back to a single submodel of interest, based on
the type of user's local data. With PRUW, the process of reading (downloading)
and writing (uploading) is carried out such that information theoretic privacy
of the updating submodel index and the values of updates is guaranteed. We
consider the practical scenario where the submodels are stored in databases
with arbitrary (heterogeneous) storage constraints, and provide a PRUW scheme
with a storage mechanism that utilizes submodel partitioning and encoding to
minimize the communication cost.
