Collaborative Differentially Private Federated Learning Framework for the Prediction of Diabetic Retinopathy

Author:

Hossain Ismail1,Puppala Sai1,Talukder Sajedul1

Affiliation:

1. Southern Illinois University,Department of Computer Science,USA

Publisher

IEEE

Reference28 articles.

1. Communication-efficient learning of deep networks from decentralized data;mcmahan;Artificial Intelligence and Statistics,0

2. A federated mining approach on predicting diabetes-related complications: Demonstration using real-world clinical data;islam;American Medical Informatics Association,0

3. Evaluating the Communication Efficiency in Federated Learning Algorithms

4. A novel hierarchical federated learning with self-regulated decentralized clustering;talukder;J Comput Sci Coll,2023

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2. SCALE: Self-Regulated Clustered FederAted LEarning in a Homogeneous Environment;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02

3. An Efficient Checkpoint Strategy for Federated Learning on Heterogeneous Fault-Prone Nodes;Electronics;2024-03-07

4. Ocular Disease Recognition via Differential Privacy and Unsupervised Domain Regularizer;IEEE Signal Processing Letters;2024

5. Diabetic Retinopathy Detection Using Novel Loss Function in Deep Learning;Communications in Computer and Information Science;2024

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