针对垂直联邦学习，提出一种高效参与者选择方法，仅选少数人就能达到全量参与效果。
VFL-RPS: Relevant Participant Selection in Vertical Federated Learning
- 基于特征相关性设计筛选机制，只选对模型有益的参与者
- 实验表明用少量精选参与者性能接近全部参与，且优于现有方法
- 适合数据特征各异的跨机构协作场景，如医疗与金融联合建模
联邦学习允许多方在不共享原始数据的前提下协作建模。然而，并非所有合作都能提升模型性能。现有研究多聚焦于水平联邦学习（HFL），假设各方特征一致，忽略了垂直联邦学习（VFL）中特征异构的实际情况。为此，本文提出VFL-RPS方法，在VFL中进行参与者选择，作为预训练步骤。在多个数据集上进行回归与分类任务测试，结果表明：仅选取少数参与者即可达到使用全部数据的性能水平，且优于现有VFL参与者选择方法。
原文摘要 · Abstract (English)
Federated Learning (FL) allows collaboration between different parties, while ensuring that the data across these parties is not shared. However, not every collaboration is helpful in terms of the resulting model performance. Therefore, it is an important challenge to select the correct participants in a collaboration. As it currently stands, most of the efforts in participant selection in the literature have focused on Horizontal Federated Learning (HFL), which assumes that all features are the same across all participants, disregarding the possibility of different features across participants which is captured in Vertical Federated Learning (VFL). To close this gap in the literature, we propose a novel method VFL-RPS for participant selection in VFL, as a pre-training step. We have tested our method on several data sets performing both regression and classification tasks, showing that our method leads to comparable results as using all data by only selecting a few participants. In addition, we show that our method outperforms existing methods for participant selection in VFL.
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