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.

翻译：在大数据与服务计算相关应用中，动态连接时常出现，尤其是Web服务中用户视角的服务质量(QoS)动态数据。这些数据被转换为包含丰富时序模式信息的高维不完整(HDI)张量。张量隐因子分解(LFT)是从HDI张量中提取此类模式的极其高效且典型的方法。然而，当前LFT模型要求QoS数据集中存储于中央位置（例如中央服务器），这对日益注重隐私的用户而言并不可行。为解决此问题，本文创新性地设计了一种基于张量隐因子分解的联邦学习方法(FL-LFT)。该方法构建了面向数据密度的联邦学习模型，使孤立用户能够在保护隐私的前提下协同训练全局LFT模型。在真实世界采集的QoS数据集上进行的大量实验表明，与最先进的联邦学习(FL)方法相比，FL-LFT在预测精度方面展现出显著提升。