  We consider information-theoretic privacy in federated submodel learning,
where a global server has multiple submodels. Compared to the privacy
considered in the conventional federated submodel learning where secure
aggregation is adopted for ensuring privacy, information-theoretic privacy
provides the stronger protection on submodel selection by the local machine. We
propose an achievable scheme that partially adopts the conventional private
information retrieval (PIR) scheme that achieves the minimum amount of
download. With respect to computation and communication overhead, we compare
the achievable scheme with a naive approach for federated submodel learning
with information-theoretic privacy.
