  In applications related to big data and service computing, dynamic
connections tend to be encountered, especially the dynamic data of
user-perspective quality of service (QoS) in Web services. They are transformed
into high-dimensional and incomplete (HDI) tensors which include abundant
temporal pattern information. Latent factorization of tensors (LFT) is an
extremely efficient and typical approach for extracting such patterns from an
HDI tensor. However, current LFT models require the QoS data to be maintained
in a central place (e.g., a central server), which is impossible for
increasingly privacy-sensitive users. To address this problem, this article
creatively designs a federated learning based on latent factorization of
tensors (FL-LFT). It builds a data-density -oriented federated learning model
to enable isolated users to collaboratively train a global LFT model while
protecting user's privacy. Extensive experiments on a QoS dataset collected
from the real world verify that FL-LFT shows a remarkable increase in
prediction accuracy when compared to state-of-the-art federated learning (FL)
approaches.
