{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T13:57:05Z","timestamp":1790517425562,"version":"4.1.0"},"reference-count":27,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technology and Health Care"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Federated learning enables collaborative medical model development without centralizing patient records; however, client-level class imbalance may reduce sensitivity to malignant cases. This study evaluated FDCTGAN, a client-local CTGAN-augmented federated learning framework for imbalanced breast cancer classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In FDCTGAN, each eligible client trained a local conditional generative adversarial network and generated synthetic malignant records equal to 50% of its local class deficit. Raw and synthetic data remained on the originating client. Only feature extrema were shared once per outer fold, while classifier parameters were exchanged through Federated Averaging. Five-fold stratified cross-validation on the Wisconsin Diagnostic Breast Cancer dataset compared FDCTGAN with Vanilla FL, Local SMOTE-FL, Centralized CTGAN-FL, and Centralized Pooled learning. Each augmentation method was evaluated using three augmentation seeds and three classifier seeds, producing nine stochastic runs per fold.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Under full-batch local training, FDCTGAN increased malignant-class recall from 0.9448\u2009\u00b1\u20090.0206 with Vanilla FL to 0.9632\u2009\u00b1\u20090.0117. However, the paired recall difference was not statistically significant using a two-sided Wilcoxon signed-rank test (p\u2009=\u20090.125; five folds), and an increase in false positives accompanied the improvement. With a batch size of 32, FDCTGAN no longer outperformed Vanilla FL in malignant recall. Local SMOTE-FL provided the strongest overall balance among the federated approaches, whereas Centralized Pooled learning achieved the highest mean accuracy and F1 score. Property-inference analysis revealed substantial information leakage from individual client updates, and CTGAN training accounted for most of the computational runtime.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>FDCTGAN represents a client-local proof of concept for addressing class imbalance in federated breast cancer classification. Its sensitivity advantage was dependent on the full-batch training protocol and was not statistically significant across the five outer folds. External validation, nested hyperparameter tuning, synthetic-data fidelity evaluation, and formal privacy-preserving mechanisms are required before clinical deployment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/09287329261484379","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T15:46:17Z","timestamp":1790091977000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["FDCTGAN: Client-local CTGAN-augmented federated learning\u00a0for imbalanced breast cancer\u00a0classification"],"prefix":"10.1177","author":[{"given":"Arash","family":"Salehpour","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Ali","family":"Balafar","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Souri","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Hali\u00e7 University, Istanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1273","volume-title":"Communication-efficient learning of deep networks from decentralised data","author":"McMahan HB","unstructured":"McMahan HB, Moore E, Ramage D, et al. Communication-efficient learning of deep networks from decentralised data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA, 20\u201322 April 2017, pp.1273\u20131282: PMLR 54."},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-69250-1"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13721-025-00512-6"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.0824-7935.2004.t01-1-00228.x"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.12.035"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124755"},{"key":"e_1_3_2_8_2","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li T","year":"2020","unstructured":"Li T, Sahu AK, Zaheer M, et al. Federated optimization in heterogeneous networks. Proc Mach Learn Syst 2020; 2: 429\u2013450.","journal-title":"Proc Mach Learn Syst"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3487767"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3283930"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/0046-8177(95)90229-5"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.24432\/C5DW2B"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.3390\/e25010088"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-95858-2"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare11243185"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105080"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01591-5"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2025.1721740"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-025-10380-y"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcard.2025.133266"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2023.100890"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10652-8"},{"key":"e_1_3_2_23_2","first-page":"19767","volume-title":"Revisiting weighted aggregation in federated learning with neural networks","author":"Li Z","unstructured":"Li Z, Lin T, Shang X, et al. Revisiting weighted aggregation in federated learning with neural networks. In: Proceedings of the 40th International Conference on Machine Learning, 23\u201329 July 2023, pp.19767\u201319788: PMLR 202."},{"key":"e_1_3_2_24_2","first-page":"7335","article-title":"Modeling tabular data using conditional GAN","volume":"32","author":"Xu L","year":"2019","unstructured":"Xu L, Skoularidou M, Cuesta-Infante A, et al. Modeling tabular data using conditional GAN. Adv Neural Inf Process Syst 2019; 32: 7335\u20137345.","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"e_1_3_2_26_2","first-page":"14774","article-title":"Deep leakage from gradients","volume":"32","author":"Zhu L","year":"2019","unstructured":"Zhu L, Liu Z, Han S. Deep leakage from gradients. 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