Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity
Federated learning (FL) enables deep learning models to be trained locally on devices without the need for data sharing, ensuring data privacy. However, when clients have uneven or imbalanced data distributions, it leads to data heterogeneity. Data heterogeneity can appear in different ways, often d...
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| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Applied System Innovation |
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| Online Access: | https://www.mdpi.com/2571-5577/8/2/28 |
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| author | Aiswariya Milan Kummaya Amudha Joseph Kumar Rajamani George Ghinea |
| author_facet | Aiswariya Milan Kummaya Amudha Joseph Kumar Rajamani George Ghinea |
| author_sort | Aiswariya Milan Kummaya |
| collection | DOAJ |
| description | Federated learning (FL) enables deep learning models to be trained locally on devices without the need for data sharing, ensuring data privacy. However, when clients have uneven or imbalanced data distributions, it leads to data heterogeneity. Data heterogeneity can appear in different ways, often due to variations in label, data distributions, feature variations, and structural inconsistencies in the images. This can significantly impact FL performance, as the global model often struggles to achieve optimal convergence. To enhance training efficiency and model performance, a common strategy in FL is to exclude clients with limited data. However, excluding such clients can raise fairness concerns, particularly for smaller populations. To understand the influence of data heterogeneity, a self-evaluating federated learning framework for heterogeneity, <b>Fed-Hetero,</b> was designed to assess the type of heterogeneity associated with the clients and provide recommendations to clients to enhance the global model’s accuracy. Fed-Hetero thus enables the clients with limited data to participate in FL processes by adopting appropriate strategies that enhance model accuracy. The results show that Fed-Hetero identifies the client with heterogeneity and provides personalized recommendations. |
| format | Article |
| id | doaj-art-40ee08b9af7c46f1b9e86ab367a42fce |
| institution | DOAJ |
| issn | 2571-5577 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied System Innovation |
| spelling | doaj-art-40ee08b9af7c46f1b9e86ab367a42fce2025-08-20T03:14:17ZengMDPI AGApplied System Innovation2571-55772025-02-01822810.3390/asi8020028Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data HeterogeneityAiswariya Milan Kummaya0Amudha Joseph1Kumar Rajamani2George Ghinea3Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeedam, Bengaluru 560035, IndiaDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeedam, Bengaluru 560035, IndiaDepartment of AI, Marwadi University, Rajkot 360003, IndiaDepartment of Computer Science, Brunel University, London UB8 3PH, UKFederated learning (FL) enables deep learning models to be trained locally on devices without the need for data sharing, ensuring data privacy. However, when clients have uneven or imbalanced data distributions, it leads to data heterogeneity. Data heterogeneity can appear in different ways, often due to variations in label, data distributions, feature variations, and structural inconsistencies in the images. This can significantly impact FL performance, as the global model often struggles to achieve optimal convergence. To enhance training efficiency and model performance, a common strategy in FL is to exclude clients with limited data. However, excluding such clients can raise fairness concerns, particularly for smaller populations. To understand the influence of data heterogeneity, a self-evaluating federated learning framework for heterogeneity, <b>Fed-Hetero,</b> was designed to assess the type of heterogeneity associated with the clients and provide recommendations to clients to enhance the global model’s accuracy. Fed-Hetero thus enables the clients with limited data to participate in FL processes by adopting appropriate strategies that enhance model accuracy. The results show that Fed-Hetero identifies the client with heterogeneity and provides personalized recommendations.https://www.mdpi.com/2571-5577/8/2/28federated learningdata heterogeneityquantity skewlabel distribution skewimage skewFed-Hetero |
| spellingShingle | Aiswariya Milan Kummaya Amudha Joseph Kumar Rajamani George Ghinea Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity Applied System Innovation federated learning data heterogeneity quantity skew label distribution skew image skew Fed-Hetero |
| title | Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity |
| title_full | Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity |
| title_fullStr | Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity |
| title_full_unstemmed | Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity |
| title_short | Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity |
| title_sort | fed hetero a self evaluating federated learning framework for data heterogeneity |
| topic | federated learning data heterogeneity quantity skew label distribution skew image skew Fed-Hetero |
| url | https://www.mdpi.com/2571-5577/8/2/28 |
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