Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications

TL;DR

This paper demonstrates the practical implementation of secure aggregation protocols in federated learning for healthcare, showing they effectively preserve privacy with minimal impact on accuracy and computational overhead.

Contribution

It implements and benchmarks two secure aggregation protocols within the Fed-BioMed framework, demonstrating their feasibility and efficiency in real-world healthcare data analysis.

Findings

SA protocols protect privacy effectively.

Minimal impact on task accuracy (less than 2%).

Low computational overhead during training.

Abstract

Deploying federated learning (FL) in real-world scenarios, particularly in healthcare, poses challenges in communication and security. In particular, with respect to the federated aggregation procedure, researchers have been focusing on the study of secure aggregation (SA) schemes to provide privacy guarantees over the model's parameters transmitted by the clients. Nevertheless, the practical availability of SA in currently available FL frameworks is currently limited, due to computational and communication bottlenecks. To fill this gap, this study explores the implementation of SA within the open-source Fed-BioMed framework. We implement and compare two SA protocols, Joye-Libert (JL) and Low Overhead Masking (LOM), by providing extensive benchmarks in a panel of healthcare data analysis problems. Our theoretical and experimental evaluations on four datasets demonstrate that SA protocols effectively protect privacy while maintaining task accuracy. Computational overhead during training is less than 1% on a CPU and less than 50% on a GPU for large models, with protection phases taking less than 10 seconds. Incorporating SA into Fed-BioMed impacts task accuracy by no more than 2% compared to non-SA scenarios. Overall this study demonstrates the feasibility of SA in real-world healthcare applications and contributes in reducing the gap towards the adoption of privacy-preserving technologies in sensitive applications.