Federated Latent Factorization of Tensors for Privacy-Preserving Representation Learning to Large-Scale Dynamic Weighted Directed Network

作者：Di Wu, Shuai Zhong, Yi He, Xin Luo, Xinbo Gao · 发表于：IEEE Transactions on Dependable and Secure Computing · 年份：2026 · DOI：10.1109/tdsc.2026.3683950 · 被引用次数：1 · 研究领域：Advanced Graph Neural Networks、Tensor decomposition and applications、Privacy-Preserving Technologies in Data

Large-scale dynamic weighted directed network (DWDN) is commonly utilized to illustrate the temporal interactions between nodes in numerous applications. Latent factorization of tensors (LFT) is a typical representation learning approach to extract the desired knowledge from a DWDN via low-rank tensor embedding. However, an existing LFT approach requires the target DWDN to be maintained in one central place like a central server, which is becoming unacceptable for users who are getting increasingly privacy-sensitive. To address this vital issue, this paper innovatively proposes a federated latent factorization of tensors (FLFT) model. It can perform accurate and privacy-preserving representation learning to a DWDN based on four-fold ideas: 1) establishing a data-density-oriented federated learning framework to enable different users to efficiently and cooperatively build a shared LFT model with keeping raw data privacy, 2) incorporating the linear biases into the local training of each user to eliminate the personalized biases or local fluctuations, 3) adopting an effective hybrid filling strategy to further protect each user's private interaction information, and 4) designing a customized nonlinear activation function to capture the nonlinear characteristics of users' interactions. Extensive experiments on four DWDNs collected from industrial applications validate that FLFT demonstrates a notable increase in accuracy compared with state-of-the-art federated and non-federated...