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Malware hunting is challenging in the IoT due to the variety of instruction set architectures of devices, as shown by the differences in the relevant characteristics of malware on different platforms. There are also serious concerns about resource utilization and privacy leaks in the development of conventional detection models. This study suggests a novel federated malware detection framework based on many\u2010objective optimization (FMDMO) for the IoT to overcome the problems. First, the framework provides a cross\u2010platform compatible basis with the federated mechanism as the backbone, while avoiding raw data sharing to improve privacy protection. Second, an intelligent optimization\u2010based client selection method is designed for four objectives: learning performance, architectural selection deviation, time consumption, and training stability, which leads malware detection to retain a high degree of cross\u2010architectural generalization while enhancing training efficiency. Based on a large IoT malware dataset we constructed, containing 62,515 malware samples across seven typical architectures, the FMDMO is evaluated comprehensively in three scenarios. The experimental results demonstrate the FMDMO substantially enhances the model's cross\u2010platform detection performance while preserving effective training and flexibility.<\/jats:p>","DOI":"10.1002\/cpe.7919","type":"journal-article","created":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T10:34:23Z","timestamp":1697538863000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Federated malware detection based on many\u2010objective optimization in cross\u2010architectural <scp>IoT<\/scp>"],"prefix":"10.1002","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0063-0044","authenticated-orcid":false,"given":"Zhigang","family":"Zhang","sequence":"first","affiliation":[{"name":"Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology  Taiyuan China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0847-3841","authenticated-orcid":false,"given":"Zhixia","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology  Taiyuan China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7388-3180","authenticated-orcid":false,"given":"Zhihua","family":"Cui","sequence":"additional","affiliation":[{"name":"Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology  Taiyuan China"}]}],"member":"311","published-online":{"date-parts":[[2023,10,17]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2935189"},{"key":"e_1_2_8_3_1","unstructured":"S. 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