Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects
With the widespread application of Artificial Intelligence (AI) and Machine Learning (ML) in medical field, early diagnosis of brain tumors has become increasingly significant. However, traditional methods face challenges such as data privacy, model interpretability, and data heterogeneity. This pap...
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03028.pdf |
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| author | Ma Yuhan |
| author_facet | Ma Yuhan |
| author_sort | Ma Yuhan |
| collection | DOAJ |
| description | With the widespread application of Artificial Intelligence (AI) and Machine Learning (ML) in medical field, early diagnosis of brain tumors has become increasingly significant. However, traditional methods face challenges such as data privacy, model interpretability, and data heterogeneity. This paper presents a detailed literature review of the application of Federated Learning (FL) in brain tumor classification, focusing on the necessity of privacy-preserving ML using Magnetic Resonance Imaging (MRI) technology. This paper analyzes various Convolutional Neural Network (CNN) models, including VGG16, ResNet50, DenseNet121, and EfficientNet, exploring their integration within the FL framework to enhance diagnostic accuracy while preserving patient data privacy. Through the discussion, core issues in this field emerge, including model interpretability, non-independent and identically distributed (Non-IID) data distributions, and computational challenges in FL architecture. Although these factors limit the widespread application of FL in medical settings, this paper also proposes potential solutions, such as improving algorithm interpretability through interpretable tools, and utilizing transfer learning and domain adaptation methods to enhance model effectiveness across different datasets. Techniques like knowledge distillation, model quantization, and pruning are proposed to enhance computational efficiency and minimize communication costs. Future research should focus on the application of these methods to enhance model reliability and efficiency. |
| format | Article |
| id | doaj-art-6669a1393dd347dab3a761eff8578e3f |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-6669a1393dd347dab3a761eff8578e3f2025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700302810.1051/itmconf/20257003028itmconf_dai2024_03028Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future ProspectsMa Yuhan0Computer Science and Technology, Anhui UniversityWith the widespread application of Artificial Intelligence (AI) and Machine Learning (ML) in medical field, early diagnosis of brain tumors has become increasingly significant. However, traditional methods face challenges such as data privacy, model interpretability, and data heterogeneity. This paper presents a detailed literature review of the application of Federated Learning (FL) in brain tumor classification, focusing on the necessity of privacy-preserving ML using Magnetic Resonance Imaging (MRI) technology. This paper analyzes various Convolutional Neural Network (CNN) models, including VGG16, ResNet50, DenseNet121, and EfficientNet, exploring their integration within the FL framework to enhance diagnostic accuracy while preserving patient data privacy. Through the discussion, core issues in this field emerge, including model interpretability, non-independent and identically distributed (Non-IID) data distributions, and computational challenges in FL architecture. Although these factors limit the widespread application of FL in medical settings, this paper also proposes potential solutions, such as improving algorithm interpretability through interpretable tools, and utilizing transfer learning and domain adaptation methods to enhance model effectiveness across different datasets. Techniques like knowledge distillation, model quantization, and pruning are proposed to enhance computational efficiency and minimize communication costs. Future research should focus on the application of these methods to enhance model reliability and efficiency.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03028.pdf |
| spellingShingle | Ma Yuhan Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects ITM Web of Conferences |
| title | Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects |
| title_full | Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects |
| title_fullStr | Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects |
| title_full_unstemmed | Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects |
| title_short | Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects |
| title_sort | federated learning for brain tumor diagnosis methods challenges and future prospects |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03028.pdf |
| work_keys_str_mv | AT mayuhan federatedlearningforbraintumordiagnosismethodschallengesandfutureprospects |