Federated Learning based Latent Factorization of Tensors for Privacy-Preserving QoS Prediction

TL;DR

This paper introduces FL-LFT, a federated learning approach for tensor latent factorization that enables privacy-preserving QoS prediction in web services, demonstrating improved accuracy over existing methods.

Contribution

The paper proposes a novel federated learning model for tensor latent factorization, addressing privacy concerns in QoS prediction without central data collection.

Findings

FL-LFT outperforms existing federated learning methods in prediction accuracy.

The approach effectively preserves user privacy while maintaining high model performance.

Experiments on real-world QoS data validate the model's effectiveness.

Abstract

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.