Federated Learning based Latent Factorization of Tensors for Privacy-Preserving QoS Prediction
This addresses the need for privacy protection in QoS prediction for users in service computing applications, though it is an incremental improvement by applying federated learning to an existing LFT method.
The paper tackles the problem of privacy-preserving QoS prediction by proposing a federated learning-based latent factorization of tensors (FL-LFT) model, which allows isolated users to collaboratively train a global model without sharing data, resulting in a remarkable increase in prediction accuracy compared to state-of-the-art federated learning approaches.
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.