{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T20:35:23Z","timestamp":1773002123941,"version":"3.50.1"},"reference-count":37,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"vor","delay-in-days":323,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306108"],"award-info":[{"award-number":["62306108"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2023AFB042"],"award-info":[{"award-number":["2023AFB042"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005408","name":"University of Electronic Science and Technology of China","doi-asserted-by":"publisher","award":["XW2022-001"],"award-info":[{"award-number":["XW2022-001"]}],"id":[{"id":"10.13039\/501100005408","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>\n                    Brain tumors account for approximately 2.5% of cancer\u2010related deaths. Accurate classification of brain tumor types is essential for timely diagnosis and enhancing survival rates. Convolutional neural networks (CNNs) have demonstrated state\u2010of\u2010the\u2010art performance in computer\u2010aided diagnosis of brain tumors; however, the quality and availability of medical data significantly influence this process. Medical data must adhere to stringent privacy regulations, such as the General Data Protection Regulation (GDPR) in the European Union and the Health Insurance Portability and Accountability Act (HIPAA) in the United States. Federated learning (FL) enables the sharing of only model update parameters during collaborative training on locally stored data. However, these parameters may inadvertently enable reconstruction of the original data. Furthermore, medical data often exhibit nonindependent and nonidentically distributed (non\u2010IID) characteristics, impeding model training performance. To address these challenges, this paper proposes a scheme that partitions confidential data into multiple segments during FL training, ensuring that only a subset exceeding a predefined threshold can reconstruct the data. The proposed scheme guarantees enhanced security, distributed control, and fault tolerance. In addition, this paper introduces a Conditional Mutual Information (CMI) regularizer to mitigate variability in model predictions. By minimizing the Kullback\u2013Leibler (KL) divergence between local and global feature distributions, the CMI regularizer substantially enhances performance and convergence stability. Extensive experiments conducted on the Figshare dataset with varying \u03b1\u2010values for data distributions validate the efficacy of the proposed model. Compared to FedAvg, FedProx, and FedDyn at\n                    <jats:italic>\u03b1<\/jats:italic>\n                    \u2009=\u20090.3, as well as the central model, the proposed model achieves a top\u20101 accuracy of 92.94% on the Figshare dataset, surpassing FedProx, FedAvg, and FedDyn by 2.42%, 2.82%, and 3.53%, respectively. Federated IID achieves performance comparable to that of the central model, further demonstrating its viability for practical applications.\n                  <\/jats:p>","DOI":"10.1155\/int\/8817677","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T02:25:32Z","timestamp":1763691932000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pragmatic Brain Tumor Imaging Classification Using Federated Learning"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8409-0263","authenticated-orcid":false,"given":"Jun","family":"Wen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9113-7130","authenticated-orcid":false,"given":"Xiaoli","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiusheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106668"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3233574"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/app12115500"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3120998"},{"key":"e_1_2_10_5_2","volume-title":"Guide to the UK General Data Protection Regulation (UK GDPR)","author":"Information Commissioner\u2019s Office","year":"2018"},{"key":"e_1_2_10_6_2","unstructured":"US Department of Health & Human Services Health Insurance Portability and Accountability Act (HIPAA) 2023."},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/msp.2020.2975749"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.10.007"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3208736"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22197157"},{"key":"e_1_2_10_11_2","doi-asserted-by":"crossref","unstructured":"HitajB. AtenieseG. andPerez-CruzF. Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security 2017 603\u2013618 https:\/\/doi.org\/10.1145\/3133956.3134012 2-s2.0-85041437863.","DOI":"10.1145\/3133956.3134012"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/comst.2020.2986024"},{"key":"e_1_2_10_13_2","doi-asserted-by":"crossref","unstructured":"MelisL. SongC. De CristofaroE. andShmatikovV. Exploiting Unintended Feature Leakage in Collaborative Learning Proceedings of the 2019 IEEE Symposium on Security and Privacy (SP) 2019 691\u2013706 https:\/\/doi.org\/10.1109\/sp.2019.00029 2-s2.0-85072921653.","DOI":"10.1109\/SP.2019.00029"},{"key":"e_1_2_10_14_2","unstructured":"BagdasaryanE. VeitA. HuaY. EstrinD. andShmatikovV. How to Backdoor Federated Learning Proceedings of the International Conference on Artificial Intelligence and Statistics 2020 2938\u20132948."},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.1049\/cmu2.12333"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3037194"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3201602"},{"key":"e_1_2_10_18_2","unstructured":"McMahanB. MooreE. RamageD. HampsonS. andArcasB. A. Communication-Efficient Learning of Deep Networks from Decentralized Data 54 Proceedings of the 20th International Conference on Artificial Intelligence and Statistics AISTATS 2017 2017 Fort Lauderdale FL USA 1273\u20131282."},{"key":"e_1_2_10_19_2","unstructured":"KarimireddyS. P. KaleS. MohriM. ReddiS. J. StichS. U. andSureshA. T. SCAFFOLD: Stochastic Controlled Averaging for Federated Learning Proceedings of the 37th International Conference on Machine Learning 2020 ICML. 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Federated Learning Based on Dynamic Regularization 9th International Conference on Learning Representations ICLR 2021 2021 Virtual Event Austria."},{"key":"e_1_2_10_27_2","volume-title":"Advances in Neural Information Processing Systems","author":"Nguyen A. T.","year":"2022"},{"key":"e_1_2_10_28_2","unstructured":"IoffeS.andSzegedyC. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Proceedings of the 32nd International Conference on Machine Learning 2015 ICML 448\u2013456."},{"key":"e_1_2_10_29_2","unstructured":"ChengJ. Brain Magnetic Resonance Imaging Tumor Dataset Figshare MRI Dataset Version 2017 5 https:\/\/figshare.com\/articles\/dataset\/brain_tumor_dataset\/1512427."},{"key":"e_1_2_10_30_2","doi-asserted-by":"publisher","DOI":"10.1007\/s44196-022-00090-9"},{"key":"e_1_2_10_31_2","doi-asserted-by":"crossref","unstructured":"CaropreseL. RugaT. VocaturoE. andZumpanoE. Revealing Brain Tumor With Federated Learning IEEE 2023 International Conference on Bioinformatics and Biomedicine (BIBM) 2023.","DOI":"10.1109\/BIBM58861.2023.10385865"},{"key":"e_1_2_10_32_2","first-page":"1023","article-title":"Intelligent Handling of Noise in Federated Learning Systems","volume":"78","author":"Babar F. F.","year":"2024","journal-title":"The Journal of Supercomputing"},{"key":"e_1_2_10_33_2","first-page":"245","article-title":"Multidisciplinary Cancer Disease Classification Using Federated Learning Models","volume":"105","author":"Abbas T.","year":"2024","journal-title":"Artificial Intelligence in Medicine"},{"key":"e_1_2_10_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/s44196-023-00397-1"},{"key":"e_1_2_10_35_2","article-title":"Deep Learning-Based Brain Tumor Architecture Using Federated Learning","volume":"145","author":"Onaizah A. 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