JoVe-FL: A Joint-Embedding Vertical Federated Learning Framework

Maria Hartmann, Grégoire Danoy, Mohammed Alswaitti, Pascal Bouvry

Abstract: Federated learning is a particular type of distributed machine learning, designed to permit the joint training of a single machine learning model by multiple participants that each possess a local dataset. A characteristic feature of federated learning strategies is the avoidance of any disclosure of client data to other participants of the learning scheme. While a wealth of well-performing solutions for different scenarios exists for Horizontal Federated Learning (HFL), to date little attention has been devoted to Vertical Federated Learning (VFL). Existing approaches are limited to narrow application scenarios where few clients participate, privacy is a main concern and the vertical distribution of client data is well-understood. In this article, we first argue that VFL is naturally applicable to another, much broader application context where sharing of data is mainly limited by technological instead of privacy constraints, such as in sensor networks or satellite swarms. A VFL s