Related Experiment Video
Updated: Jan 7, 2026
04:48
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
3.3K
FeSEC: A Secure and Efficient Federated Learning Framework for Medical Imaging.
1Department of Radiation Oncology, Columbia University Irving Medical Center, New York 10032, USA.
Proceedings of Spie--The International Society for Optical Engineering
|December 29, 2025
Summary
Federated learning (FL) in medical imaging faces privacy and communication challenges. The proposed FeSEC framework enhances FL security and efficiency, improving COVID-19 detection accuracy with reduced communication costs.
Area of Science:
- Medical Imaging
- Machine Learning
- Data Privacy
Background:
- Federated learning (FL) leverages distributed data for improved medical imaging model generalization.
- FL offers partial privacy but risks compromise during model parameter exchange.
- Communication overhead is a significant challenge in FL, especially with complex models and distributed collaborators.
Purpose of the Study:
- To propose FeSEC, a secure and efficient FL framework.
- To address privacy concerns and communication bottlenecks in medical imaging FL.
- To enhance the performance of FL models in distributed healthcare settings.
Main Methods:
- Implemented a sparse compression algorithm for efficient inter-hospital communication.
- Integrated homomorphic encryption with differential privacy for secure model exchange.
- Evaluated the framework on a COVID-19 detection task.
Main Results:
- FeSEC substantially improves accuracy and privacy preservation compared to FedAvg.
- Achieved significant reductions in communication costs (less than 10% of FedAvg).
- Demonstrated enhanced FL model performance in a real-world medical imaging scenario.
Conclusions:
- FeSEC offers a viable solution for secure and efficient federated learning in medical imaging.
- The framework effectively balances privacy, communication efficiency, and model accuracy.
- FeSEC shows promise for global health applications requiring collaborative medical data analysis.
