A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning

2025-01-22

AI Summary

In medical imaging federated learning, balancing privacy preservation and communication efficiency remains challenging. To address this, we propose FAS—a novel framework integrating selective homomorphic encryption (SHE), differential privacy, and bit-level perturbation, enabling secure aggregation while significantly reducing communication and computational overhead. Its key innovation is the first-ever synergistic design of SHE and bit-level perturbation, eliminating the need for pretraining and overcoming the performance bottlenecks of fully homomorphic encryption. Evaluated within the Flower framework, FAS achieves a 90% speedup in training time and reduces total execution time by 20% compared to fully homomorphic alternatives, while attaining state-of-the-art security guarantees. This framework delivers a substantive advance in the privacy-efficiency trade-off, making it particularly suitable for resource-constrained, cross-institutional medical imaging modeling.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Medical and Biological Imaging
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Privacy-enhancing technologies
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Federated learning is a machine learning method that supports training models on decentralized devices or servers, where each holds its local data, removing the need for data exchange. This approach is especially useful in healthcare, as it enables training on sensitive data without needing to share them. The nature of federated learning necessitates robust security precautions due to data leakage concerns during communication. To address this issue, we propose a new approach that employs selective encryption, homomorphic encryption, differential privacy, and bit-wise scrambling to minimize data leakage while achieving good execution performance. Our technique , FAS (fast and secure federated learning) is used to train deep learning models on medical imaging data. We implemented our technique using the Flower framework and compared with a state-of-the-art federated learning approach that also uses selective homomorphic encryption. Our experiments were run in a cluster of eleven physical machines to create a real-world federated learning scenario on different datasets. We observed that our approach is up to 90% faster than applying fully homomorphic encryption on the model weights. In addition, we can avoid the pretraining step that is required by our competitor and can save up to 20% in terms of total execution time. While our approach was faster, it obtained similar security results as the competitor.

Problem

• Privacy Protection
• Federated Learning
• Medical Image Analysis

Innovation

• FAS
• Differential Privacy
• Homomorphic Encryption