FedDMF: Privacy-Preserving User Attribute Prediction using Deep Matrix
  Factorization
Ming Cheung
User attribute prediction is a crucial task in various industries. However,
sharing user data across different organizations faces challenges due to
privacy concerns and legal requirements regarding personally identifiable
information. Regulations such as the General Data Protection Regulation (GDPR)
in the European Union and the Personal Information Protection Law of the
People's Republic of China impose restrictions on data sharing. To address the
need for utilizing features from multiple clients while adhering to legal
requirements, federated learning algorithms have been proposed. These
algorithms aim to predict user attributes without directly sharing the data.
However, existing approaches typically rely on matching users across companies,
which can result in dishonest partners discovering user lists or the inability
to utilize all available features. In this paper, we propose a novel algorithm
for predicting user attributes without requiring user matching. Our approach
involves training deep matrix factorization models on different clients and
sharing only the item vectors. This allows us to predict user attributes
without sharing the user vectors themselves. The algorithm is evaluated using
the publicly available MovieLens dataset and demonstrate that it achieves
similar performance to the FedAvg algorithm, reaching 96% of a single model's
accuracy. The proposed algorithm is particularly well-suited for improving
customer targeting and enhancing the overall customer experience. This paper
presents a valuable contribution to the field of user attribute prediction by
offering a novel algorithm that addresses some of the most pressing privacy
concerns in this area.