解决跨客户端特征选择难题，提升垂直联邦学习精度
ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learning
- 引入跨客户端特征交互机制，多阶段集成选择关键特征
- 在多个真实数据集上准确率超越现有最优方法
- 适合需要隐私保护且特征分布不均的联邦学习场景
垂直联邦学习（VFL）允许跨客户端的纵向数据协作训练模型。由于数据分布在不同客户端，特征选择（FS）在此场景中至关重要。各客户端对重叠样本拥有不同的特征子集，因此识别并选择最相关特征成为复杂而关键的任务。以往方法主要关注客户端内特征选择，忽视了客户端间的特征交互，导致模型性能不佳。本文提出ICAFS，一种考虑跨客户端交互的多阶段集成特征选择方法。通过条件特征合成与多个可学习特征选择器结合，利用合成嵌入实现选择器间的集成特征选择。该方法规避了私有梯度共享限制，支持使用真实数据和优化后的嵌入进行模型训练。在多个真实数据集上的实验表明，ICAFS在预测准确率上优于当前最先进的方法。
原文摘要 · Abstract (English)
Vertical federated learning (VFL) enables a paradigm for vertically partitioned data across clients to collaboratively train machine learning models. Feature selection (FS) plays a crucial role in Vertical Federated Learning (VFL) due to the unique nature that data are distributed across multiple clients. In VFL, different clients possess distinct subsets of features for overlapping data samples, making the process of identifying and selecting the most relevant features a complex yet essential task. Previous FS efforts have primarily revolved around intra-client feature selection, overlooking vital feature interaction across clients, leading to subpar model outcomes. We introduce ICAFS, a novel multi-stage ensemble approach for effective FS in VFL by considering inter-client interactions. By employing conditional feature synthesis alongside multiple learnable feature selectors, ICAFS facilitates ensemble FS over these selectors using synthetic embeddings. This method bypasses the limitations of private gradient sharing and allows for model training using real data with refined embeddings. Experiments on multiple real-world datasets demonstrate that ICAFS surpasses current state-of-the-art methods in prediction accuracy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。