Byzantine-Resilient Federated Learning Leveraging Confidence Score to Identify Retinal Disease
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2024, 00, pp. 81-88
- Issue Date:
- 2024-01-29
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| Filename | Description | Size | |||
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| Byzantine-Resilient_Federated_Learning_Leveraging_Confidence_Score_to_Identify_Retinal_Disease.pdf | Published version | 739.26 kB |
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Federated learning is a distributed machine learning paradigm that enables multiple actors to collaboratively train a common model without sharing their local data thus addressing data privacy issues especially in sensitive domains such as healthcare However federated learning is vulnerable to poisoning attacks where malicious Byzantine clients can manipulate their local updates to degrade the performance or compromise the privacy of the global model To mitigate this problem this paper proposes a novel method that reduces the influence of malicious clients based on their confidence We evaluate our method on the Retinal OCT dataset consisting of age related macular degeneration and diabetic macular edema using InceptionV3 and VGG19 architecture The proposed technique significantly improves the global model s precision recall F1 score and area under the receiver operating characteristic curve AUC ROC for both InceptionV3 and VGG19 For InceptionV3 precision rises from 0 869 to 0 906 recall rises from 0 836 to 0 889 and F1 score rises from 0 852 to 0 898 For VGG19 precision rises from 0 958 to 0 963 recall rises from 0 917 to 0 941 and F1 score rises from 0 937 to 0 952
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