提出单步通信的垂直联邦学习方法，仅需与活跃方交互一次。
Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Need
- 各参与方先本地无监督学习表征，再由活跃方蒸馏知识。
- 在部分数据对齐场景下，F1和准确率均优于SplitNN等方法。
- 适合通信资源受限、数据部分对齐的联邦学习应用。
现有垂直联邦学习（VFL）方法在真实且数据分区不一致的场景下表现不佳，通信成本高且操作复杂。本文提出主动参与方中心的垂直联邦学习（APC-VFL），在参与者间训练数据部分对齐时表现优异。其核心是各参与方先进行本地无监督表征学习，再由活跃方执行知识蒸馏，仅需一次通信即可完成。相比SplitNN或VFedTrans等方法，APC-VFL在三个主流VFL数据集上，随着对齐数据比例下降，仍持续在F1、准确率和通信开销上保持领先。
原文摘要 · Abstract (English)
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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