Adding Data Quality to Federated Learning Performance Improvement
Massive data generation from Internet of Things (IoT) devices increases the demand for efficient data analysis to extract relevant and actionable insights. As a result, Federated Learning (FL) allows IoT devices to collaborate in Artificial Intelligence (AI) training models while preserving data pri...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11029230/ |
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| author | Ernesto Gurgel Valente Neto Solon Alves Peixoto Valderi Reis Quietinho Leithardt Juan Francisco de Paz Santana Julio C. S. Dos Anjos |
| author_facet | Ernesto Gurgel Valente Neto Solon Alves Peixoto Valderi Reis Quietinho Leithardt Juan Francisco de Paz Santana Julio C. S. Dos Anjos |
| author_sort | Ernesto Gurgel Valente Neto |
| collection | DOAJ |
| description | Massive data generation from Internet of Things (IoT) devices increases the demand for efficient data analysis to extract relevant and actionable insights. As a result, Federated Learning (FL) allows IoT devices to collaborate in Artificial Intelligence (AI) training models while preserving data privacy. However, selecting high-quality data for training remains a critical challenge in FL environments with non-independent and identically distributed (non-iid) data. Poor-quality data introduces errors, delays convergence, and increases computational costs. This study develops a data quality analysis algorithm for both FL and centralized environments to address these challenges. The proposed algorithm reduces computational costs, eliminates unnecessary data processing, and accelerates the convergence of AI models. The experiments utilized the MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets, and performance evaluation was based on main literature metrics, including accuracy, recall, F1 score, and precision. Results show a maximum observed execution time reduction of up to 56.49%, with an accuracy loss of approximately 0.50%. |
| format | Article |
| id | doaj-art-66ad9fc86cf34677aca0b8869c31147e |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-66ad9fc86cf34677aca0b8869c31147e2025-08-20T02:45:49ZengIEEEIEEE Access2169-35362025-01-011312662312664810.1109/ACCESS.2025.357830111029230Adding Data Quality to Federated Learning Performance ImprovementErnesto Gurgel Valente Neto0https://orcid.org/0000-0002-9881-199XSolon Alves Peixoto1https://orcid.org/0000-0002-3864-2506Valderi Reis Quietinho Leithardt2https://orcid.org/0000-0003-0446-9271Juan Francisco de Paz Santana3https://orcid.org/0000-0001-9461-7922Julio C. S. Dos Anjos4https://orcid.org/0000-0003-3623-2762PPGETI, Federal University of Ceará, Fortaleza, BrazilDepartment of Data Science, Federal University of Ceará Campus Itapajé, Itapajé, BrazilInstituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Lisboa, PortugalExpert Systems and Applications Laboratory, University of Salamanca, Salamanca, SpainDepartment of Data Science, Federal University of Ceará Campus Itapajé, Graduate Program in Teleinformatics Engineering (PPGETI/UFC) Technological Center Campus of Pici, Ceará, Fortaleza, BrazilMassive data generation from Internet of Things (IoT) devices increases the demand for efficient data analysis to extract relevant and actionable insights. As a result, Federated Learning (FL) allows IoT devices to collaborate in Artificial Intelligence (AI) training models while preserving data privacy. However, selecting high-quality data for training remains a critical challenge in FL environments with non-independent and identically distributed (non-iid) data. Poor-quality data introduces errors, delays convergence, and increases computational costs. This study develops a data quality analysis algorithm for both FL and centralized environments to address these challenges. The proposed algorithm reduces computational costs, eliminates unnecessary data processing, and accelerates the convergence of AI models. The experiments utilized the MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets, and performance evaluation was based on main literature metrics, including accuracy, recall, F1 score, and precision. Results show a maximum observed execution time reduction of up to 56.49%, with an accuracy loss of approximately 0.50%.https://ieeexplore.ieee.org/document/11029230/Data qualitydeep learningfederated learningIoTIIDnon-IID |
| spellingShingle | Ernesto Gurgel Valente Neto Solon Alves Peixoto Valderi Reis Quietinho Leithardt Juan Francisco de Paz Santana Julio C. S. Dos Anjos Adding Data Quality to Federated Learning Performance Improvement IEEE Access Data quality deep learning federated learning IoT IID non-IID |
| title | Adding Data Quality to Federated Learning Performance Improvement |
| title_full | Adding Data Quality to Federated Learning Performance Improvement |
| title_fullStr | Adding Data Quality to Federated Learning Performance Improvement |
| title_full_unstemmed | Adding Data Quality to Federated Learning Performance Improvement |
| title_short | Adding Data Quality to Federated Learning Performance Improvement |
| title_sort | adding data quality to federated learning performance improvement |
| topic | Data quality deep learning federated learning IoT IID non-IID |
| url | https://ieeexplore.ieee.org/document/11029230/ |
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