  Recently, Niu, et. al. introduced a new variant of Federated Learning (FL),
called Federated Submodel Learning (FSL). Different from traditional FL, each
client locally trains the submodel (e.g., retrieved from the servers) based on
its private data and uploads a submodel at its choice to the servers. Then all
clients aggregate all their submodels and finish the iteration. Inevitably, FSL
introduces two privacy-preserving computation tasks, i.e., Private Submodel
Retrieval (PSR) and Secure Submodel Aggregation (SSA). Existing work fails to
provide a loss-less scheme, or has impractical efficiency. In this work, we
leverage Distributed Point Function (DPF) and cuckoo hashing to construct a
practical and light-weight secure FSL scheme in the two-server setting. More
specifically, we propose two basic protocols with few optimisation techniques,
which ensures our protocol practicality on specific real-world FSL tasks. Our
experiments show that our proposed protocols can finish in less than 1 minute
when weight sizes $\leq 2^{15}$, we also demonstrate protocol efficiency by
comparing with existing work and by handling a real-world FSL task.
