Event-Driven Online Vertical Federated Learning

TL;DR

This paper introduces an event-driven online vertical federated learning framework that handles asynchronous, event-based data streams across clients, improving stability and efficiency in non-stationary environments.

Contribution

It pioneers the integration of event-driven mechanisms and dynamic local regret into online VFL, addressing challenges of asynchronous data and non-convex models.

Findings

Enhanced stability over existing frameworks in non-stationary data

Significant reduction in communication and computation costs

Effective handling of asynchronous, event-based data streams

Abstract

Online learning is more adaptable to real-world scenarios in Vertical Federated Learning (VFL) compared to offline learning. However, integrating online learning into VFL presents challenges due to the unique nature of VFL, where clients possess non-intersecting feature sets for the same sample. In real-world scenarios, the clients may not receive data streaming for the disjoint features for the same entity synchronously. Instead, the data are typically generated by an \emph{event} relevant to only a subset of clients. We are the first to identify these challenges in online VFL, which have been overlooked by previous research. To address these challenges, we proposed an event-driven online VFL framework. In this framework, only a subset of clients were activated during each event, while the remaining clients passively collaborated in the learning process. Furthermore, we incorporated \emph{dynamic local regret (DLR)} into VFL to address the challenges posed by online learning problems with non-convex models within a non-stationary environment. We conducted a comprehensive regret analysis of our proposed framework, specifically examining the DLR under non-convex conditions with event-driven online VFL. Extensive experiments demonstrated that our proposed framework was more stable than the existing online VFL framework under non-stationary data conditions while also significantly reducing communication and computation costs.