{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T12:24:32Z","timestamp":1762777472017,"version":"build-2065373602"},"reference-count":12,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T00:00:00Z","timestamp":1759795200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Real\u2010time multi\u2010object detection on smartphones requires a careful balance of accuracy, latency, energy efficiency, and data privacy. We introduce\n                    <jats:italic>FedEdgeDetect<\/jats:italic>\n                    , a unified framework that combines federated learning with edge\u2010assisted inference to address these challenges holistically. The system incorporates a hardware\u2010aware YOLOv5s variant with lightweight attention modules for efficient on\u2010device execution. A capability\u2010clustered federated training protocol is designed to ensure privacy through differential noise injection and secure aggregation, while reducing communication overhead. At inference time, a dynamic controller adaptively partitions computation between the device and edge, optimizing for real\u2010time performance and energy consumption. Experiments across diverse datasets and devices demonstrate that FedEdgeDetect consistently improves detection accuracy, accelerates inference, enhances energy efficiency, and enforces strong privacy guarantees, outperforming existing mobile detection baselines.\n                  <\/jats:p>","DOI":"10.1002\/itl2.70145","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:28:53Z","timestamp":1759883333000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Federated Edge Intelligence: A Collaborative Learning Framework for Multi\u2010Object Detection on Mobile Platforms"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6767-4435","authenticated-orcid":false,"given":"Miao","family":"Yan","sequence":"first","affiliation":[{"name":"School of Computer and Artificial Intelligence Jilin Technology College of Electronic Information  Jilin China"}]}],"member":"311","published-online":{"date-parts":[[2025,10,7]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57959-7"},{"key":"e_1_2_8_3_1","article-title":"Distilling the Knowledge in a Neural Network","author":"Hinton G.","year":"2015","journal-title":"arXiv"},{"key":"e_1_2_8_4_1","first-page":"1273","volume-title":"PMLR","author":"McMahan B.","year":"2017"},{"key":"e_1_2_8_5_1","first-page":"429","article-title":"Federated Optimization in Heterogeneous Networks","volume":"2","author":"Li T.","year":"2020","journal-title":"Proceedings of Machine Learning and Systems"},{"issue":"1","key":"e_1_2_8_6_1","first-page":"1","article-title":"A New Lightweight Network Based on MobileNetV3","volume":"16","author":"Zhao L.","year":"2022","journal-title":"KSII Transactions on Internet and Information Systems"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2952146"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3093337.3037698"},{"issue":"8","key":"e_1_2_8_9_1","first-page":"1905","article-title":"FedEdge: Convergence\u2010Aware Federated Learning in Mobile Edge Computing","volume":"32","author":"Zhou F.","year":"2020","journal-title":"IEEE Transactions on Parallel and Distributed Systems"},{"key":"e_1_2_8_10_1","first-page":"740","volume-title":"Computer Vision","author":"Lin T. Y.","year":"2014"},{"issue":"11","key":"e_1_2_8_11_1","first-page":"7810","article-title":"VisDrone\u2010DET2021: The Vision Meets Drone Object Detection Challenge Results","volume":"44","author":"Zhu P.","year":"2021","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_2_8_12_1","article-title":"Yolov4: Optimal Speed and Accuracy of Object Detection","author":"Bochkovskiy A.","year":"2020","journal-title":"arXiv"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3158253"}],"container-title":["Internet Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.70145","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T12:19:47Z","timestamp":1762777187000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/itl2.70145"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,7]]},"references-count":12,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1002\/itl2.70145"],"URL":"https:\/\/doi.org\/10.1002\/itl2.70145","archive":["Portico"],"relation":{},"ISSN":["2476-1508","2476-1508"],"issn-type":[{"type":"print","value":"2476-1508"},{"type":"electronic","value":"2476-1508"}],"subject":[],"published":{"date-parts":[[2025,10,7]]},"assertion":[{"value":"2025-07-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70145"}}