  In this work, we present a federated version of the state-of-the-art Neural
Collaborative Filtering (NCF) approach for item recommendations. The system,
named FedNCF, enables learning without requiring users to disclose or transmit
their raw data. Data localization preserves data privacy and complies with
regulations such as the GDPR. Although federated learning enables model
training without local data dissemination, the transmission of raw clients'
updates raises additional privacy issues. To address this challenge, we
incorporate a privacy-preserving aggregation method that satisfies the security
requirements against an honest but curious entity. We argue theoretically and
experimentally that existing aggregation algorithms are inconsistent with
latent factor model updates. We propose an enhancement by decomposing the
aggregation step into matrix factorization and neural network-based averaging.
Experimental validation shows that FedNCF achieves comparable recommendation
quality to the original NCF system, while our proposed aggregation leads to
faster convergence compared to existing methods. We investigate the
effectiveness of the federated recommender system and evaluate the
privacy-preserving mechanism in terms of computational cost.
