Event-Driven Online Vertical Federated Learning
Keywords: Vertical Federated Learning, Online Learning, Event Driven
TL;DR: Applying online learning to VFL is not straightforward due to its inherent nature.
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 *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 *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.
Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 9074
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