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Appl."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>With the rapid development of smart device technology, the current version of the Internet of Things (IoT) is moving towards a multimedia IoT because of multimedia data. This innovative concept seamlessly integrates multimedia data with the IoT-Edge Continuum. Recently, a distributed learning framework has shown promise in revolutionizing various industries, including smart cities, healthcare, etc. However, these applications may face challenges, such as the presence of malicious devices that invade the privacy of other devices or corrupt uploaded model parameters. Additionally, the existing synchronous federated learning (FL) methods face challenges in effectively training models on local datasets due to the diversity of IoT devices. To tackle these concerns, we propose an efficient and privacy-enhanced asynchronous FL approach for multimedia data in edge-based IoT. In contrast to traditional FL methods, our approach combines revocable attribute-based encryption (RABE) and differential privacy (DP). This guarantees the privacy of the entire process while allowing seamless collaboration between multiple devices and the aggregation server during model training. Also, this combination brings a dynamic nature to the system. Furthermore, we utilize an asynchronous weight-based aggregation algorithm to improve the efficiency of training and the quality of the final returned model. Our proposed scheme is confirmed by theoretical safety proofs and experimental results with multimedia data. Performance evaluation shows that our framework reduces the cryptography runtime by 63.3% and the global model aggregation time by 61.9% compared to cutting-edge schemes. Moreover, our accuracy is comparable to the most primitive FL schemes, maintaining 86.7%, 70.8%, and 86.1% on MNIST, CIFAR-10, and Fashion-MNIST, respectively. The experimental results highlight the remarkable practicality, resilience and effectiveness of the proposed scheme.<\/jats:p>","DOI":"10.1145\/3688002","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T12:33:51Z","timestamp":1723811631000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Efficient and Privacy-Enhanced Asynchronous Federated Learning for Multimedia Data in Edge-Based IoT"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6137-6667","authenticated-orcid":false,"given":"Hu","family":"Xiong","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5675-0406","authenticated-orcid":false,"given":"Hang","family":"Yan","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1569-9657","authenticated-orcid":false,"given":"Mohammad S.","family":"Obaidat","sequence":"additional","affiliation":[{"name":"King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China, Department of Computational Intelligence, School of Computing, SRM University, Chennai, India, and School of Engineering, The Amity University, Noida, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7931-7558","authenticated-orcid":false,"given":"Jingxue","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0691-2724","authenticated-orcid":false,"given":"Mingsheng","family":"Cao","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China and Ningbo WebKing Technology Joint Stock Company, Ltd., Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5324-2156","authenticated-orcid":false,"given":"Sachin","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Galgotias College of Engineering and Technology, Greater Noida, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3800-0933","authenticated-orcid":false,"given":"Kadambri","family":"Agarwal","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4929-5383","authenticated-orcid":false,"given":"Saru","family":"Kumari","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Chaudhary Charan Singh University, Meerut, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"e_1_3_1_2_1","doi-asserted-by":"crossref","unstructured":"Jiayi Sun Wensheng Gan Han-Chieh Chao S. 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