{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:35:27Z","timestamp":1760060127737,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Program of Hubei Province, China","award":["2024BAB031","2024BAB016"],"award-info":[{"award-number":["2024BAB031","2024BAB016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The widespread adoption of Internet of Things (IoT) technology has significantly expanded the scale at which devices are connected, posing new challenges to maintaining symmetry in network management. Traditional centralized identification architectures adopt a symmetric processing paradigm in which all device data are uniformly transmitted to the cloud for processing. However, this rigid symmetric structure fails to accommodate the asymmetric distribution typical of IoT edge devices. To address these challenges, this paper proposes an asymmetric identification framework based on cloud\u2013edge collaboration, exploring a high-performance, resource-efficient, and privacy-preserving solution for IoT device identification. The proposed region-specific personalized algorithm (FedRP) introduces a region-specific, personalized identification approach grounded in federated learning principles. Firstly, FedRP leverages a decentralized processing framework to enhance data security by processing data locally. Secondly, it employs a personalized federated learning framework to optimize local models, thus improving identification accuracy and effectiveness. Finally, FedRP strategically separates the personalized parameters of transformer-based blocks from shared parameters and selectively transmits them, reducing the burden on network resources. Comprehensive comparative experiments demonstrate the efficacy of the proposed approach for large-scale IoT environments, which are characterized by numerous devices and complex network conditions.<\/jats:p>","DOI":"10.3390\/sym17081308","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T07:45:31Z","timestamp":1755071131000},"page":"1308","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FedRP: Region-Specific Personalized Identification for Large-Scale IoT Systems"],"prefix":"10.3390","volume":"17","author":[{"given":"Yuhan","family":"Jin","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Internet of Intelligence, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"School of Economics, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Cao","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Internet of Intelligence, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junfei","family":"Wang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Internet of Intelligence, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"School of Economics, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benkuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Internet of Intelligence, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiacheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Internet of Intelligence, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingdong","family":"Liu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Internet of Intelligence, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuwei","family":"Guo","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Internet of Intelligence, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Xu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Internet of Intelligence, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/MCOM.2013.6461195","article-title":"Improving network management with software defined networking","volume":"51","author":"Kim","year":"2013","journal-title":"IEEE Commun. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8229","DOI":"10.1109\/JIOT.2022.3150363","article-title":"Recent Advances on Federated Learning for Cybersecurity and Cybersecurity for Federated Learning for Internet of Things","volume":"9","author":"Ghimire","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1109\/TSC.2024.3349408","article-title":"Joint Optimization of Service Deployment and Request Routing for Microservices in Mobile Edge Computing","volume":"17","author":"Peng","year":"2024","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zha, Z., He, J., Zhen, L., Yu, M., Dong, C., Li, Z., Wu, G., Zuo, H., and Peng, K. (2024). A BiGRU Model Based on the DBO Algorithm for Cloud-Edge Communication Networks. Appl. Sci., 14.","DOI":"10.3390\/app142210155"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2366","DOI":"10.1109\/COMST.2022.3200740","article-title":"Pervasive AI for IoT Applications: A Survey on Resource-Efficient Distributed Artificial Intelligence","volume":"24","author":"Baccour","year":"2022","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"9312","DOI":"10.1109\/JIOT.2023.3323704","article-title":"Multidrone Parcel Delivery via Public Vehicles: A Joint Optimization Approach","volume":"11","author":"Deng","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4059","DOI":"10.1109\/JIOT.2022.3203249","article-title":"A Survey on IoT Intrusion Detection: Federated Learning, Game Theory, Social Psychology, and Explainable AI as Future Directions","volume":"10","author":"Arisdakessian","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/s10723-024-09759-1","article-title":"On the Joint Design of Microservice Deployment and Routing in Cloud Data Centers","volume":"22","author":"Xu","year":"2024","journal-title":"J. Grid Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"33847","DOI":"10.1109\/JIOT.2024.3434583","article-title":"Clustering-Based Collaborative Storage for Blockchain in IoT Systems","volume":"11","author":"Peng","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12758","DOI":"10.1109\/TII.2024.3424347","article-title":"Collaborative Deployment and Routing of Industrial Microservices in Smart Factories","volume":"20","author":"Hu","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4551","DOI":"10.1109\/TNSE.2024.3404271","article-title":"Obstacle-Aware Multicast Routing Algorithm for Large-Scale LEO Constellations","volume":"11","author":"Wang","year":"2024","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"122442","DOI":"10.1016\/j.eswa.2023.122442","article-title":"Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, applications, challenges and future research directions","volume":"240","author":"Habbal","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6024","DOI":"10.1109\/TNSE.2024.3436616","article-title":"Delay-Aware Optimization of Fine-Grained Microservice Deployment and Routing in Edge via Reinforcement Learning","volume":"11","author":"Peng","year":"2024","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3635030","article-title":"Industrial Internet of Things Ecosystems Security and Digital Forensics: Achievements, Open Challenges, and Future Directions","volume":"56","author":"Kebande","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1109\/COMST.2015.2476338","article-title":"Device Fingerprinting in Wireless Networks: Challenges and Opportunities","volume":"18","author":"Xu","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7295","DOI":"10.1109\/JIOT.2020.2984030","article-title":"Detecting Behavioral Change of IoT Devices Using Clustering-Based Network Traffic Modeling","volume":"7","author":"Sivanathan","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1109\/JSAC.2019.2904364","article-title":"AuDI: Toward Autonomous IoT Device-Type Identification Using Periodic Communication","volume":"37","author":"Marchal","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_18","unstructured":"Meidan, Y., Bohadana, M., Shabtai, A., Ochoa, M., Tippenhauer, N.O., Guarnizo, J.D., and Elovici, Y. (2017). Detection of Unauthorized IoT Devices Using Machine Learning Techniques. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Aksoy, A., and Gunes, M.H. (2019, January 20\u201324). Automated IoT Device Identification using Network Traffic. Proceedings of the ICC 2019\u20142019 IEEE International Conference on Communications (ICC), Shanghai, China.","DOI":"10.1109\/ICC.2019.8761559"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Desai, B.A., Divakaran, D.M., Nevat, I., Peter, G.W., and Gurusamy, M. (2019, January 7\u201311). A feature-ranking framework for IoT device classification. Proceedings of the 2019 11th International Conference on Communication Systems & Networks (COMSNETS), Bengaluru, India.","DOI":"10.1109\/COMSNETS.2019.8711210"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Albarqouni, S., Bakas, S., Bano, S., Cardoso, M.J., Khanal, B., Landman, B., Li, X., Qin, C., Rekik, I., and Rieke, N. (2022). FedAP: Adaptive Personalization in Federated Learning for Non-IID Data. Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health, Springer.","DOI":"10.1007\/978-3-031-18523-6"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bao, J., Hamdaoui, B., and Wong, W.K. (2020, January 15\u201319). IoT Device Type Identification Using Hybrid Deep Learning Approach for Increased IoT Security. Proceedings of the 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus.","DOI":"10.1109\/IWCMC48107.2020.9148110"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xu, H., Zhang, Z., Yu, X., Wu, Y., Zha, Z., Xu, B., Xu, W., Hu, M., and Peng, K. (2024). Targeted Training Data Extraction\u2014Neighborhood Comparison-Based Membership Inference Attacks in Large Language Models. Appl. Sci., 14.","DOI":"10.3390\/app14167118"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yin, F., Yang, L., Wang, Y., and Dai, J. (February, January 30). IoT ETEI: End-to-End IoT Device Identification Method. Proceedings of the 2021 IEEE Conference on Dependable and Secure Computing (DSC), Fukushima, Japan.","DOI":"10.1109\/DSC49826.2021.9346251"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.future.2023.10.011","article-title":"Adversarial attacks and defenses on ML- and hardware-based IoT device fingerprinting and identification","volume":"152","author":"Bovet","year":"2024","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1109\/JIOT.2020.3018677","article-title":"Zero-Bias Deep Learning for Accurate Identification of Internet-of-Things (IoT) Devices","volume":"8","author":"Liu","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_27","first-page":"1273","article-title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","volume":"54","author":"McMahan","year":"2017","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Feng, K., Luo, L., Xia, Y., Luo, B., He, X., Li, K., Zha, Z., Xu, B., and Peng, K. (2024). Optimizing Microservice Deployment in Edge Computing with Large Language Models: Integrating Retrieval Augmented Generation and Chain of Thought Techniques. Symmetry, 16.","DOI":"10.3390\/sym16111470"},{"key":"ref_29","first-page":"7611","article-title":"Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization","volume":"33","author":"Wang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_30","first-page":"37379","article-title":"Anchor Sampling for Federated Learning with Partial Client Participation","volume":"202","author":"Wu","year":"2023","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_31","first-page":"19767","article-title":"Revisiting Weighted Aggregation in Federated Learning with Neural Networks","volume":"202","author":"Li","year":"2023","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.neucom.2021.08.141","article-title":"FedSim: Similarity guided model aggregation for Federated Learning","volume":"483","author":"Palihawadana","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","article-title":"Federated Learning in Mobile Edge Networks: A Comprehensive Survey","volume":"22","author":"Lim","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1898","DOI":"10.1109\/TCCN.2021.3101239","article-title":"Edge Device Identification Based on Federated Learning and Network Traffic Feature Engineering","volume":"8","author":"He","year":"2022","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5926","DOI":"10.1109\/JIOT.2020.3032544","article-title":"Blockchain-Based Federated Learning for Device Failure Detection in Industrial IoT","volume":"8","author":"Zhang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1109\/JIOT.2021.3077803","article-title":"Federated-Learning-Based Anomaly Detection for IoT Security Attacks","volume":"9","author":"Mothukuri","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xu, G., Xu, S., Fan, X., Cao, Y., Mao, Y., Xie, Y., and Chen, X.B. (2025). RAT Ring: Event Driven Publish\/Subscribe Communication Protocol for IIoT by Report and Traceable Ring Signature. IEEE Trans. Ind. Inform., 1\u20139.","DOI":"10.1109\/TII.2025.3567265"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Cao, Z., Huang, L., Wang, T., Wang, Y., Shi, J., Zhu, A., Shi, T., and Snoussi, H. (2025). Understanding the Dimensional Need of Noncontrastive Learning. IEEE Trans. Cybern., 1\u201314.","DOI":"10.1109\/TCYB.2025.3577745"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1109\/TNSE.2024.3505986","article-title":"DCMM: Dynamic Cluster-Based Mobile Node Migration Scheme for Blockchain Collaborative Storage in Mobile IoT Networks","volume":"12","author":"Peng","year":"2025","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.jnca.2016.01.008","article-title":"A survey on data leakage prevention systems","volume":"62","author":"Alneyadi","year":"2016","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_41","first-page":"10043","article-title":"Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles between Client Data Subspaces","volume":"37","author":"Vahidian","year":"2023","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_42","first-page":"8432","article-title":"FedProto: Federated Prototype Learning across Heterogeneous Clients","volume":"36","author":"Tan","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_43","unstructured":"Lu, W., Wang, J., Chen, Y., Qin, X., Xu, R., Dimitriadis, D., and Qin, T. (2022). Personalized Federated Learning with Adaptive Batchnorm for Healthcare. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"11052","DOI":"10.1109\/JIOT.2021.3051480","article-title":"Cost-Aware Feature Selection for IoT Device Classification","volume":"8","author":"Chakraborty","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hamad, S.A., Zhang, W.E., Sheng, Q.Z., and Nepal, S. (2019, January 5\u20138). IoT Device Identification via Network-Flow Based Fingerprinting and Learning. Proceedings of the 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications\/13th IEEE International Conference on Big Data Science And Engineering (TrustCom\/BigDataSE), Rotorua, New Zealand.","DOI":"10.1109\/TrustCom\/BigDataSE.2019.00023"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1109\/JIOT.2018.2865604","article-title":"DEFT: A Distributed IoT Fingerprinting Technique","volume":"6","author":"Thangavelu","year":"2019","journal-title":"IEEE Internet Things J."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/8\/1308\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:26:04Z","timestamp":1760034364000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/8\/1308"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,13]]},"references-count":46,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["sym17081308"],"URL":"https:\/\/doi.org\/10.3390\/sym17081308","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2025,8,13]]}}}