Summary of Towards Privacy-preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified Resnet Architecture, by Mohamad Haj Fares et al.
Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture
by Mohamad Haj Fares, Ahmed Mohamed Saad Emam Saad
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed federated learning framework combines local differential privacy with secure aggregation using Secure Multi-Party Computation for medical image classification, addressing concerns over privacy in healthcare. A modified ResNet architecture optimized for differential privacy, called DPResNet, is also introduced. The BloodMNIST benchmark dataset is used to simulate a realistic data-sharing environment across different hospitals, considering the unique privacy challenges posed by federated healthcare data. Experimental results show that the privacy-preserving federated model achieves accuracy levels comparable to non-private models, outperforming traditional approaches while maintaining strict data confidentiality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a way for hospitals to share medical images without compromising patient privacy. It uses a special type of computer program called Secure Multi-Party Computation to combine private medical image datasets from different hospitals. The team also created a new kind of neural network, called DPResNet, that is designed specifically for preserving patient privacy. They tested their approach using a fake dataset that mimics real-world hospital data and found that it can accurately classify medical images while keeping the sensitive information confidential. |
Keywords
» Artificial intelligence » Federated learning » Image classification » Neural network » Resnet
