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arXiv · 2410.17648

Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Need

Jon Irureta·Jon Imaz·Aizea Lojo·Javier Fernandez-Marques·Marco González·Iñigo Perona
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Abstract

Existing Vertical FL (VFL) methods often struggle with realistic and unaligned data partitions, and incur into high communication costs and significant operational complexity. This work introduces a novel approach to VFL, Active Participant Centric VFL (APC-VFL), that excels in scenarios when data samples among participants are partially aligned at training. Among its strengths, APC-VFL only requires a single communication step with the active participant. This is made possible through a local and unsupervised representation learning stage at each participant followed by a knowledge distillation step in the active participant. Compared to other VFL methods such as SplitNN or VFedTrans, APC-VFL consistently outperforms them across three popular VFL datasets in terms of F1, accuracy and communication costs as the ratio of aligned data is reduced.

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Jon Irureta, Jon Imaz, Aizea Lojo, Javier Fernandez-Marques, Marco González, Iñigo Perona. 2024-10-23. Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Need. https://arxiv.org/abs/2410.17648

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