{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T13:18:31Z","timestamp":1785417511473,"version":"3.56.0"},"reference-count":38,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T00:00:00Z","timestamp":1783814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009620","name":"Department of Science and Technology of Gansu Province","doi-asserted-by":"publisher","award":["24JRRA573"],"award-info":[{"award-number":["24JRRA573"]}],"id":[{"id":"10.13039\/501100009620","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Gansu University of Political Science and Law","award":["GZF2025XQN15"],"award-info":[{"award-number":["GZF2025XQN15"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China Youth Science","doi-asserted-by":"publisher","award":["62103178"],"award-info":[{"award-number":["62103178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62103178"],"award-info":[{"award-number":["62103178"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Federated object detection allows distributed IoT cameras to learn a shared detector without exposing raw images. Its performance is limited by non-IID scenes, unequal device resources, intermittent links, and selfish participation. We propose GO-FedDet, a game-theoretic optimized federated detection framework for heterogeneous IoT application. Client participation is formulated as a Stackelberg game, selection is formulated as an exact-potential resource game, and the equilibrium is embedded into a proximal detection objective. A utility-aligned aggregation balances detection contribution, communication\/energy cost, and long-term fairness. We prove the existence of a Stackelberg equilibrium and the finite-improvement convergence of the selection game, and we derive a non-convex convergence bound under client drift and partial participation. Experiments on heterogeneous edge object detection show that GO-FedDet improves accuracy, lowers communication cost, and stabilizes fairness compared with representative federated baselines.<\/jats:p>","DOI":"10.3390\/s26144420","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T08:53:58Z","timestamp":1783932838000},"page":"4420","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Game-Theoretic Optimized Federated Learning for Heterogeneous IoT Object Detection"],"prefix":"10.3390","volume":"26","author":[{"given":"Zepeng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Cyberspace Security, Gansu University of Political Science and Law, Lanzhou 730070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7335-995X","authenticated-orcid":false,"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Automation, University of Science and Technology of China, Hefei 230027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bhatia, S., Alsuwailam, R.I., Roy, D.G., and Mashat, A. 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