Enhancing Privacy in Federated Learning: A Practical Assessment of Combined PETs in a Cross-Silo Setting
Published in 2024 ACM Workshop on Information Hiding and Multimedia Security, 2024
Recommended citation: Loureiro-Acuña, J., Martínez-Luaña, X., Padín-Torrente, H., Jiménez-Balsa, G., García-Pagán, C., & Ortega-Fernandez, I. (2024). Enhancing Privacy in Federated Learning: A Practical Assessment of Combined PETs in a Cross-Silo Setting. Proceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security, 265–270. doi:10.1145/3658664.3659661 https://dl.acm.org/doi/abs/10.1145/3658664.3659661
Federated Learning (FL) has emerged as a revolutionary machine learning setting to enable collaborative training in a privacy-preserving way. However, recent research has showcased significant privacy attacks that pose a serious threat to the proliferation of FL as a technology designed to safeguard privacy during training with sensitive data from multiple entities. The rapid evolution of Privacy Enhancing Technologies offers promising methods for securing data inputs and outputs in FL scenarios. This paper evaluates and benchmarks the practical application of two PET methods, which has been integrated within a custom-built FL platform. The work conducts a comparative analysis of several privacy techniques applied to Federated Learning scenarios, with a primary focus on computational and communication performance.