Enhancing global model accuracy in federated learning with deep neuro-fuzzy clustering cyclic algorithm
In recent years, with the increasing importance of privacy protection, many laws and regulations have standardized data usage, requiring companies to obtain user consent to access personal data. This has become more challenging for models that require large amounts of data for training. Therefore, t...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824012584 |
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| author | Chin-Feng Lai Ying-Hsun Lai Ming-Chin Kao Mu-Yen Chen |
| author_facet | Chin-Feng Lai Ying-Hsun Lai Ming-Chin Kao Mu-Yen Chen |
| author_sort | Chin-Feng Lai |
| collection | DOAJ |
| description | In recent years, with the increasing importance of privacy protection, many laws and regulations have standardized data usage, requiring companies to obtain user consent to access personal data. This has become more challenging for models that require large amounts of data for training. Therefore, the concept of federated learning was proposed in 2016, aiming to train models with different clients without sharing data to ensure data privacy. However, federated learning faces several challenges, including heterogeneous devices, data security, data heterogeneity, communication costs, and training time costs. This study focuses on addressing the issue of data heterogeneity, where the data distribution among participating clients differs significantly, leading to poor performance of the aggregated model after training. To tackle this problem, we propose a federated clustering cyclic algorithm, which involves two-step clustering of clients to make the data distribution of each cluster approach independent and identically distributed. We also introduce deep neural fuzzy methods to handle fuzzy, uncertain, or incomplete data. According to experimental results, the proposed deep neuro-fuzzy clustered cyclic algorithm outperforms methods such as FedAvg, FedProx, and CyclicFL on various non-IID datasets, with accuracy approaching that of centralized learning in certain experiments. This indicates that the deep neural fuzzy methods and clustering cyclic algorithm DNCC presented in this study can improve the accuracy of global models, especially in increasingly non-IID scenarios. Furthermore, we extend this method to big data processing to cope with more complex data environments. |
| format | Article |
| id | doaj-art-afc4301757c04d249becfb02f2cd0704 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-afc4301757c04d249becfb02f2cd07042025-01-29T05:00:09ZengElsevierAlexandria Engineering Journal1110-01682025-01-01112474486Enhancing global model accuracy in federated learning with deep neuro-fuzzy clustering cyclic algorithmChin-Feng Lai0Ying-Hsun Lai1Ming-Chin Kao2Mu-Yen Chen3National Cheng Kung University, No. 1, University Rd., Tainan, 70101, TaiwanNational Taitung University, No. 369, Sec. 2, University Rd., Taitung, 950309, TaiwanIndustrial Technology Research Institute, 195, Sec. 4, Chung Hsing Rd., Hsinchu, 300096, TaiwanNational Cheng Kung University, No. 1, University Rd., Tainan, 70101, Taiwan; Corresponding author.In recent years, with the increasing importance of privacy protection, many laws and regulations have standardized data usage, requiring companies to obtain user consent to access personal data. This has become more challenging for models that require large amounts of data for training. Therefore, the concept of federated learning was proposed in 2016, aiming to train models with different clients without sharing data to ensure data privacy. However, federated learning faces several challenges, including heterogeneous devices, data security, data heterogeneity, communication costs, and training time costs. This study focuses on addressing the issue of data heterogeneity, where the data distribution among participating clients differs significantly, leading to poor performance of the aggregated model after training. To tackle this problem, we propose a federated clustering cyclic algorithm, which involves two-step clustering of clients to make the data distribution of each cluster approach independent and identically distributed. We also introduce deep neural fuzzy methods to handle fuzzy, uncertain, or incomplete data. According to experimental results, the proposed deep neuro-fuzzy clustered cyclic algorithm outperforms methods such as FedAvg, FedProx, and CyclicFL on various non-IID datasets, with accuracy approaching that of centralized learning in certain experiments. This indicates that the deep neural fuzzy methods and clustering cyclic algorithm DNCC presented in this study can improve the accuracy of global models, especially in increasingly non-IID scenarios. Furthermore, we extend this method to big data processing to cope with more complex data environments.http://www.sciencedirect.com/science/article/pii/S1110016824012584Federated learningNon-IID dataDeep neuro-fuzzy clusteringCyclic trainingModel convergence |
| spellingShingle | Chin-Feng Lai Ying-Hsun Lai Ming-Chin Kao Mu-Yen Chen Enhancing global model accuracy in federated learning with deep neuro-fuzzy clustering cyclic algorithm Alexandria Engineering Journal Federated learning Non-IID data Deep neuro-fuzzy clustering Cyclic training Model convergence |
| title | Enhancing global model accuracy in federated learning with deep neuro-fuzzy clustering cyclic algorithm |
| title_full | Enhancing global model accuracy in federated learning with deep neuro-fuzzy clustering cyclic algorithm |
| title_fullStr | Enhancing global model accuracy in federated learning with deep neuro-fuzzy clustering cyclic algorithm |
| title_full_unstemmed | Enhancing global model accuracy in federated learning with deep neuro-fuzzy clustering cyclic algorithm |
| title_short | Enhancing global model accuracy in federated learning with deep neuro-fuzzy clustering cyclic algorithm |
| title_sort | enhancing global model accuracy in federated learning with deep neuro fuzzy clustering cyclic algorithm |
| topic | Federated learning Non-IID data Deep neuro-fuzzy clustering Cyclic training Model convergence |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824012584 |
| work_keys_str_mv | AT chinfenglai enhancingglobalmodelaccuracyinfederatedlearningwithdeepneurofuzzyclusteringcyclicalgorithm AT yinghsunlai enhancingglobalmodelaccuracyinfederatedlearningwithdeepneurofuzzyclusteringcyclicalgorithm AT mingchinkao enhancingglobalmodelaccuracyinfederatedlearningwithdeepneurofuzzyclusteringcyclicalgorithm AT muyenchen enhancingglobalmodelaccuracyinfederatedlearningwithdeepneurofuzzyclusteringcyclicalgorithm |