The Sherpa.ai Blind Vertical Federated Learning Paradigm to Minimize the Number of Communications

TL;DR

This paper introduces SBVFL, a novel vertical federated learning paradigm that significantly reduces communication costs by decoupling node updates, enabling practical privacy-preserving collaborative training in sensitive domains.

Contribution

The paper proposes SBVFL, a new VFL approach that minimizes communication overhead by decoupling node updates from the server, enhancing privacy and efficiency.

Findings

Reduces communication by approximately 99% compared to standard VFL.

Maintains accuracy and robustness despite reduced communication.

Enables practical deployment of VFL in sensitive sectors.

Abstract

Federated Learning (FL) enables collaborative decentralized training across multiple parties (nodes) while keeping raw data private. There are two main paradigms in FL: Horizontal FL (HFL), where all participant nodes share the same feature space but hold different samples, and Vertical FL (VFL), where participants hold complementary features for the same samples. While HFL is widely adopted, VFL is employed in domains where nodes hold complementary features about the same samples. Still, VFL presents a significant limitation: the vast number of communications required during training. This compromises privacy and security, and can lead to high energy consumption, and in some cases, make model training unfeasible due to the high number of communications. In this paper, we introduce Sherpa.ai Blind Vertical Federated Learning (SBVFL), a novel paradigm that leverages a distributed training mechanism enhanced for privacy and security. Decoupling the vast majority of node updates from the server dramatically reduces node-server communication. Experiments show that SBVFL reduces communication by ~99% compared to standard VFL while maintaining accuracy and robustness. Therefore, SBVFL enables practical, privacy-preserving VFL across sensitive domains, including healthcare, finance, manufacturing, aerospace, cybersecurity, and the defense industry.