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Federated learning (FL), which provides a decentralized method of model training without jeopardizing data privacy, has become a paradigm-shifting breakthrough in artificial intelligence (AI). This work addresses homogeneous federated learning in healthcare. The homogeneous federated learning (HFL) aims to train models across several devices or nodes while maintaining data security and privacy. The same data distribution and model architecture are usually shared by all nodes involved in HFL. In this paper, we propose a HFL model for diabetes prediction case study. The dataset is divided into five segments and assign one segment to each client, then apply four machine learning classification algorithms to find some metrics such as accuracy, precision, recall, and f1 score. The healthcare field has been experiencing a major impact from federated learning. FL has several other uses outside of the medical field, like IoT and Android, Blockchains, among others. This paper primarily addresses federated learning about data privacy in the medical field.

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A Homogeneous Federated Learning Approach Toward Prediction of Diabetes

  • 会议论文

  • 发表日期：

    2025年

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  • Rahul Karmakar,
  • Arindam Sarkar,
  • Debraj Malik,
  • Priyabrata Sain

摘要

Federated learning (FL), which provides a decentralized method of model training without jeopardizing data privacy, has become a paradigm-shifting breakthrough in artificial intelligence (AI). This work addresses homogeneous federated learning in healthcare. The homogeneous federated learning (HFL) aims to train models across several devices or nodes while maintaining data security and privacy. The same data distribution and model architecture are usually shared by all nodes involved in HFL. In this paper, we propose a HFL model for diabetes prediction case study. The dataset is divided into five segments and assign one segment to each client, then apply four machine learning classification algorithms to find some metrics such as accuracy, precision, recall, and f1 score. The healthcare field has been experiencing a major impact from federated learning. FL has several other uses outside of the medical field, like IoT and Android, Blockchains, among others. This paper primarily addresses federated learning about data privacy in the medical field.