Vertical Federated Learning
Vertical Federated Learning (VFL) enables multiple organizations to collaboratively train machine learning models without sharing raw data, particularly suited for tabular datasets with aligned sample IDs but disjoint feature spaces. Despite its growing relevance in privacy-sensitive sectors such as finance and healthcare, publicly available benchmarks for VFL on tabular data remain limited. This paper introduces and categorizes a collection of real-world tabular datasets tailored for VFL research, highlighting their feature distribution, domain applicability, and security relevance.
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With the continuous improvement in the computational capabilities of edge devices such as intelligent sensors in the Industrial Internet of Things, these sensors are no longer limited to mere data collection but are increasingly capable of performing complex computational tasks. This advancement provides both the motivation and the foundation for adopting distributed learning approaches. This study focuses on an industrial assembly line scenario where multiple sensors, distributed across various locations, sequentially collect real-time data characterized by distinct feature spaces.
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