Federated learning (FL) is a new paradigm that allows several parties to train a model together without sharing their proprietary data. This paper investigates vertical federated learning, which addresses scenarios in which collaborating organizations own data from the same set of users but with differing features. The survey provides an overview of how five alternative Vertical Federated
Learning frameworks function, as well as a description of their performance and security assurances. A thorough comparison of how each of the alternatives handles the trade-offs between data privacy, framework performance, and model performance is extracted based on the examined frameworks.
This allows the reader to form an opinion about benefits and disadvantages of various techniques across the vertical federated learning landscape.