Vertical Federated Learning: Taxonomies, Threats, and Prospects

Abstract

Federated learning (FL) is the most popular distributed machine learning technique. FL allows machine-learning models to be trained without acquiring raw data to a single point for processing. Instead, local models are trained with local data; the models are then shared and combined. This approach preserves data privacy as locally trained models are shared instead of the raw data themselves. Broadly, FL can be divided into horizontal federated learning (HFL) and vertical federated learning (VFL). For the former, different parties hold different samples over the same set of features; for the latter, different parties hold different feature data belonging to the same set of samples. In a number of practical scenarios, VFL is more relevant than HFL as different companies (e.g., bank and retailer) hold different features (e.g., credit history and shopping history) for the same set of customers. Although VFL is an emerging area of research, it is not well-established compared to HFL. Besides, VFL-related studies are dispersed, and their connections are not intuitive. Thus, this survey aims to bring these VFL-related studies to one place. Firstly, we classify existing VFL structures and algorithms. Secondly, we present the threats from security and privacy perspectives to VFL. Thirdly, for the benefit of future researchers, we discussed the challenges and prospects of VFL in detail.

Task-Driven Transferred Vertical Federated Deep Learning for Multivariate Internet of Things Time-Series Analysis

“…For the experiments in Section 5, we constructed a multivariate IoT time series dataset with a similar domain to [1][2][3][4][5]. In addition, for each dataset, three target task models according to the type of FM part were designed as in Figure 1 and used in the experiment.…”

“…Vertical federated learning is a distributed learning technique based on vertically partitioned data where features are distributed in the sample direction [1]. Each party, which is the distributed learning unit of vertical federated learning, is generally a service or a silo, a large-scale storage unit.…”

“…Using vertical federated deep learning (VFDL) methods, we can train a deep learning model for multivariate IoT time-series data distributed across services. VFDL methods are techniques for distributed learning of deep learning models based on vertically partitioned data architectures, where features are distributed [1]. We classified various existing VFDL methods into two categories from the design approach perspective.…”

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

“…This occurs when two datasets are created using the same samples but differ in extraction [23]. VFL is often related to an enterprise setting where the number of clients participating is much smaller than HFL, but privacy matters are paramount [24,25]. In FTL, devices have different samples, and the extracted features differ.…”