  Non-negative matrix factorization (NMF) with missing-value completion is a
well-known effective Collaborative Filtering (CF) method used to provide
personalized user recommendations. However, traditional CF relies on the
privacy-invasive collection of users' explicit and implicit feedback to build a
central recommender model. One-shot federated learning has recently emerged as
a method to mitigate the privacy problem while addressing the traditional
communication bottleneck of federated learning. In this paper, we present the
first unsupervised one-shot federated CF implementation, named FedSPLIT, based
on NMF joint factorization. In our solution, the clients first apply local CF
in-parallel to build distinct client-specific recommenders. Then, the
privacy-preserving local item patterns and biases from each client are shared
with the processor to perform joint factorization in order to extract the
global item patterns. Extracted patterns are then aggregated to each client to
build the local models via knowledge distillation. In our experiments, we
demonstrate the feasibility of our approach with standard recommendation
datasets. FedSPLIT can obtain similar results than the state of the art (and
even outperform it in certain situations) with a substantial decrease in the
number of communications.
