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

Blind V ertical Federated Learning Paradigm to Minimize the Number of Communications Sherpa.ai 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 V ertical 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 V ertical Federated Learning (SBVFL), a novel paradigm that leverages a distributed training mechanism enhanced for privacy and security. De-coupling 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. Federated Learning (FL) [1] enables collaborative training across multiple nodes (parties, clients, devices) while keeping raw data decentralized, sharing only model updates instead of centralizing data as in traditional Machine Learning (ML). FL is typically categorized into Horizontal FL (HFL), where nodes share the same feature space but hold different samples, and V ertical FL (VFL), where nodes hold data with different feature spaces for the same set of samples [2].