Comparative Analysis of Federated Association Rules in a Simulated Environment for Medical Applications

This article examines some of the most relevant algorithms for association rule mining in a medical context, within the framework of unsupervised Federated Learning (FL) in a simulated environment. Unlike traditional algorithms that rely on centralized databases, FL operates on decentralized devices...

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Vydáno v:IEEE journal of biomedical and health informatics Ročník PP; s. 1 - 11
Hlavní autoři: Panos-Basterra, Juan, Rivas, Jose M., Morcillo-Jimenez, Roberto, Fernandez-Basso, Carlos, Ruiz, M. Dolores, Martin-Bautista, Maria J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 28.04.2025
Témata:
Association rule learning
Association rules
Bioinformatics
Data privacy
Federated learning
Federated Mining
healthcare data
Homomorphic encryption
Itemsets
Machine learning algorithms
Medical services
Privacy-preserving
Proposals
Protocols
Shamir secret sharing
Vectors
ISSN:2168-2194, 2168-2208, 2168-2208
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Abstract This article examines some of the most relevant algorithms for association rule mining in a medical context, within the framework of unsupervised Federated Learning (FL) in a simulated environment. Unlike traditional algorithms that rely on centralized databases, FL operates on decentralized devices, prioritizing data privacy and algorithm efficiency. This emerging paradigm in machine learning and data mining aims to preserve privacy in edge networks. In this study, the privacy of subject data is ensured as the algorithms operate and collaborate between nodes, sharing only the results of their computations while keeping raw data encapsulated and encrypted. The work evaluates key aspects such as execution time and encryption/decryption efficiency in edge networks. This analysis is motivated by the increasing demand for data analysis in the healthcare sector, where maintaining data privacy is critical due to the proliferation of data privacy regulations.
AbstractList This article examines some of the most relevant algorithms for association rule mining in a medical context, within the framework of unsupervised Federated Learning (FL) in a simulated environment. Unlike traditional algorithms that rely on centralized databases, FL operates on decentralized devices, prioritizing data privacy and algorithm efficiency. This emerging paradigm in machine learning and data mining aims to preserve privacy in edge networks. In this study, the privacy of subject data is ensured as the algorithms operate and collaborate between nodes, sharing only the results of their computations while keeping raw data encapsulated and encrypted. The work evaluates key aspects such as execution time and encryption/decryption efficiency in edge networks. This analysis is motivated by the increasing demand for data analysis in the healthcare sector, where maintaining data privacy is critical due to the proliferation of data privacy regulations.
This article examines some of the most relevant algorithms for association rule mining in a medical context, within the framework of unsupervised Federated Learning (FL) in a simulated environment. Unlike traditional algorithms that rely on centralized databases, FL operates on decentralized devices, prioritizing data privacy and algorithm efficiency. This emerging paradigm in machine learning and data mining aims to preserve privacy in edge networks. In this study, the privacy of subject data is ensured as the algorithms operate and collaborate between nodes, sharing only the results of their computations while keeping raw data encapsulated and encrypted. The work evaluates key aspects such as execution time and encryption/decryption efficiency in edge networks. This analysis is motivated by the increasing demand for data analysis in the healthcare sector, where maintaining data privacy is critical due to the proliferation of data privacy regulations.This article examines some of the most relevant algorithms for association rule mining in a medical context, within the framework of unsupervised Federated Learning (FL) in a simulated environment. Unlike traditional algorithms that rely on centralized databases, FL operates on decentralized devices, prioritizing data privacy and algorithm efficiency. This emerging paradigm in machine learning and data mining aims to preserve privacy in edge networks. In this study, the privacy of subject data is ensured as the algorithms operate and collaborate between nodes, sharing only the results of their computations while keeping raw data encapsulated and encrypted. The work evaluates key aspects such as execution time and encryption/decryption efficiency in edge networks. This analysis is motivated by the increasing demand for data analysis in the healthcare sector, where maintaining data privacy is critical due to the proliferation of data privacy regulations.
Author Morcillo-Jimenez, Roberto
Fernandez-Basso, Carlos
Ruiz, M. Dolores
Martin-Bautista, Maria J.
Panos-Basterra, Juan
Rivas, Jose M.
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/40293898$$D View this record in MEDLINE/PubMed
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Snippet This article examines some of the most relevant algorithms for association rule mining in a medical context, within the framework of unsupervised Federated...
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SubjectTerms Association rule learning
Association rules
Bioinformatics
Data privacy
Federated learning
Federated Mining
healthcare data
Homomorphic encryption
Itemsets
Machine learning algorithms
Medical services
Privacy-preserving
Proposals
Protocols
Shamir secret sharing
Vectors
Title Comparative Analysis of Federated Association Rules in a Simulated Environment for Medical Applications
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https://www.ncbi.nlm.nih.gov/pubmed/40293898
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