Federated Learning (FL) is a distributed machine learning paradigm that enables multiple data holders to collaborate on building machine learning models while preserving the privacy of their data. FL can be categorized as horizontal or vertical, depending on the distribution characteristics of the data. Specifically, horizontal FL uses data partitioned in the sample space, whereas vertical FL uses data partitioned in the feature space. Traditional vertical FL methods aim to facilitate collaboration among clients to infer a single global target. However, these methods may be impractical because each client often has a unique target to be inferred. In this paper, we propose a novel vertical FL method, called personalized vertical federated learning (pvFed), which addresses this limitation by allowing each client to perform inferences specific to their individual task. To the best of our knowledge, no existing method currently addresses this limitation. The objective of pvFed is to construct a global model that generates a representation vector to support client inference. The global model, constructed using distillation and dimensionality reduction, takes a sample ID common to all clients as input and outputs a sample‐specific representation vector. Clients utilize the intermediate representation of their own model and the representation vectors output by the global model for inference. Because these vectors are not dependent on client‐specific tasks, clients can repurpose them for any additional tasks. Our experiments, conducted on two distinct data types—image and tabular data sets, under a vertical partitioning where each client had its own specific task, demonstrated the efficacy of vectors generated by the global model in pvFed for client inference. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

中文翻译：

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pvFed：针对客户特定任务的个性化垂直联合学习

联邦学习 (FL) 是一种分布式机器学习范例，使多个数据持有者能够协作构建机器学习模型，同时保护其数据的隐私。根据数据的分布特征，FL 可以分为水平或垂直。具体来说，水平FL使用在样本空间中划分的数据，而垂直FL使用在特征空间中划分的数据。传统的垂直 FL 方法旨在促进客户之间的协作，以推断单一的全球目标。然而，这些方法可能不切实际，因为每个客户通常都有一个独特的要推断的目标。在本文中，我们提出了一种新颖的垂直 FL 方法，称为个性化垂直联合学习 (pvFed)，它通过允许每个客户端执行特定于其个人任务的推理来解决此限制。据我们所知，目前没有任何现有方法可以解决此限制。pvFed 的目标是构建一个全局模型，生成表示向量以支持客户端推理。使用蒸馏和降维构建的全局模型将所有客户端通用的样本 ID 作为输入，并输出样本特定的表示向量。客户端利用自己模型的中间表示和全局模型输出的表示向量进行推理。由于这些向量不依赖于客户特定的任务，因此客户可以将它们重新用于任何其他任务。我们的实验在两种不同的数据类型（图像和表格数据集）上进行，在垂直分区下，每个客户端都有自己的特定任务，证明了 pvFed 中全局模型生成的向量用于客户端推理的功效。© 2024 日本电气工程师协会和 Wiley periodicals LLC。