{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T13:02:33Z","timestamp":1789995753649,"version":"4.0.1"},"reference-count":37,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":242,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Healthcare Tech Letters"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Skin cancer is among the most common malignant tumors worldwide, and early detection is essential to improve patient survival and recovery. Conventional AI\u2010based diagnostic approaches rely on centralized data, which raises security and privacy concerns. This paper presents an implementation of federated learning that addresses these challenges by enabling collaborative model training among simulated distributed client nodes while ensuring that raw patient data remains confidential. A convolutional neural network architecture, ResNet50, is employed at client nodes, with preprocessing steps including image augmentation, contrast enhancement, and lesion segmentation to improve feature extraction on the International Skin Imaging Collaboration (ISIC) dataset. Multiple aggregation algorithms are implemented at the central server, along with both vertical and horizontal federated learning settings. Their performance is evaluated using metrics such as precision, accuracy, recall, and F1\u2010score. In the horizontal federated learning setting, the FedNova aggregation approach outperformed other methods, achieving an accuracy of 76.45%, F1\u2010score of 0.73, and a recall of 0.72, demonstrating enhanced overall classification performance among the evaluated aggregation algorithms and stable convergence across clients. These results highlight the impact of federated learning settings and aggregation strategies on overall skin cancer detection performance.<\/jats:p>","DOI":"10.1049\/htl2.70096","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T07:26:47Z","timestamp":1788161207000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Impact of Aggregation Functions and Learning Settings in Privacy\u2010Preserving Federated Learning for Skin Cancer Detection"],"prefix":"10.1049","volume":"13","author":[{"given":"Bela","family":"Shrimali","sequence":"first","affiliation":[{"name":"Unitedworld Institute of Technology Karnavati University Gandhinagar India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5466-2048","authenticated-orcid":false,"given":"Rebakah","family":"Geddam","sequence":"additional","affiliation":[{"name":"Unitedworld Institute of Technology Karnavati University Gandhinagar India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hemant","family":"Ghayvat","sequence":"additional","affiliation":[{"name":"Computer Science and Media Technology Department Faculty of Technology Linnaeus University V\u00e4xj\u00f6 Sweden"},{"name":"IMT, Department of Humanities and Technology Roskilde University Roskilde Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rushi","family":"Shah","sequence":"additional","affiliation":[{"name":"Institute of Technology Nirma University Gujarat India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohit","family":"Ginglani","sequence":"additional","affiliation":[{"name":"Institute of Technology Nirma University Gujarat India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"e_1_2_11_2_1","unstructured":"International Agency for Research on Cancer (IARC) \u201cIARC Report on Skin Cancer\u201d (IARC 2026)."},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010022\u201035007\u20100"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107318"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare11243185"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.860532"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3370635"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/technologies12090151"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102572"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2025.1659026"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110500"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13193053"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3290283"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2024.101496"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.107627"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-89230-7"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2024.103692"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2026.109066"},{"key":"e_1_2_11_19_1","first-page":"52","article-title":"GBA\u2010NET: A Gated Bottleneck and Attention\u2010Driven Architecture for Robust Ischemic Stroke Segmentation Across CT and DWI","volume":"25","author":"Ganie S.","year":"2026","journal-title":"Applied and Computational Mathematics"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.70096"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.60087\/jklst.vol2.n2.p384"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-022-01647-y"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107330"},{"key":"e_1_2_11_24_1","unstructured":"mnist ham10000 \u201cHAM 1000 Dataset\u201d (2024)."},{"key":"e_1_2_11_25_1","unstructured":"N. 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