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However, the lack of labeled data, visual similarity between skin diseases, and restriction on private data sharing remain the major challenges in skin lesion diagnosis. In this article, first, we propose a federated contrastive learning framework to break down data silos and enhance the generalizability of diagnostic model to unseen data. Subsequently, by combining data features from different participated nodes, the proposed framework can improve the performance of contrastive training. To extract discriminative features during on-device training, we propose a contrastive learning based intelligent skin lesion diagnosis scheme in edge computing networks. Specifically, a contrastive learning based dual encoder network is designed to overcome training sample scarcity by fully leveraging unlabeled samples for performance improvement. Meanwhile, we devise a maximum mean discrepancy based supervised contrastive loss function, which can efficiently explore complex intra-class and inter-class variances of samples. Finally, the diagnosis simulations demonstrate that compared with existing methods, our proposed scheme can achieve superior accuracy in both on-device training and distributed training scenarios.<\/jats:p>","DOI":"10.1145\/3595186","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T12:13:20Z","timestamp":1682943200000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Skin Lesion Intelligent Diagnosis in Edge Computing Networks: An FCL Approach"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9414-2805","authenticated-orcid":false,"given":"Yanhang","family":"Shi","sequence":"first","affiliation":[{"name":"Nanjing University of Posts and Telecommunications"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6082-5955","authenticated-orcid":false,"given":"Xue","family":"Li","sequence":"additional","affiliation":[{"name":"Women\u2019s Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital)"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7375-0393","authenticated-orcid":false,"given":"Siguang","family":"Chen","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications"}]}],"member":"320","published-online":{"date-parts":[[2023,6,15]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113922"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32778-1_8"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.11.042"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2018.01.007"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISRITI48646.2019.9034624"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30642-7_30"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87199-4_36"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9534376"},{"key":"e_1_3_2_11_2","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems (NIPS\u201920)","author":"Grill Jean-Bastien","year":"2020","unstructured":"Jean-Bastien Grill, Florian Strub, Florent Altch\u00e9, Corentin Tallec, Pierre H. 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