Secure Federated Learning Algorithms for Vertical and Combined Data Partitioning

With the growing need for collaborative machine learning across institutions holding sensitive data, ensuring data privacy without compromising model performance has become an important challenge. This work introduces secure federated learning algorithms that use encryption and masking techniques to protect the privacy of data during collaborative model training. Three federated learning algorithms were developed: one for vertical federated learning and two combining horizontal and vertical data partitioning. The proposed algorithms are designed such that participating clients communicate only with the server, even when data exchange between clients is required. This exchange occurs through the server with the help of encryption and masking. The performance of the algorithms, evaluated in terms of accuracy and loss, shows competitive results. The accuracy remains unchanged compared to the centralised scenario for the vertical federated learning algorithm and one of the combined federated learning algorithms, and it remains highly competitive with the other combined federated learning algorithm. The privacy analyses conducted as part of this work demonstrate no risk of data leakage ensuring that no party involved can infer sensitive information.