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Bibliographic Details
Main Authors: Irureta, Jon, Imaz, Jon, Lojo, Aizea, Fernandez-Marques, Javier, González, Marco, Perona, Iñigo
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2410.17648
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Table of Contents:
  • 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.