{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T12:53:05Z","timestamp":1774356785990,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"vor","delay-in-days":36,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Internet Things"],"DOI":"10.1007\/s43926-026-00308-8","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T12:03:36Z","timestamp":1771243416000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A privacy preserving split learning framework with adaptive inference selection for IoT security"],"prefix":"10.1007","volume":"6","author":[{"given":"V. Santhosh","family":"Kumar","sequence":"first","affiliation":[]},{"given":"Dhiraj","family":"Sunehra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,16]]},"reference":[{"key":"308_CR1","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1109\/tce.2022.3232478","volume":"69","author":"CK Wu","year":"2022","unstructured":"Wu CK, Cheng C-T, Uwate Y, Chen G, Mumtaz S, Tsang KF. State-of-the-Art and research opportunities for Next-Generation consumer electronics. IEEE Trans Consum Electron. 2022;69:937\u201348. https:\/\/doi.org\/10.1109\/tce.2022.3232478.","journal-title":"IEEE Trans Consum Electron"},{"key":"308_CR2","doi-asserted-by":"publisher","unstructured":"Lee K, Majd NE. Anomaly Detection and Attack Classification in IoT Networks Using Machine Learning. IEEE International Conference on Performance, Computing and Communications 2023:453\u20138. https:\/\/doi.org\/10.1109\/ipccc59175.2023.10253835","DOI":"10.1109\/ipccc59175.2023.10253835"},{"key":"308_CR3","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1109\/jiot.2021.3121517","volume":"9","author":"R Kumar","year":"2021","unstructured":"Kumar R, Swarnkar M, Singal G, Kumar N. IoT network Traffic Classification Using Machine Learning Algorithms: An Experimental analysis. IEEE Internet Things J. 2021;9:989\u20131008. https:\/\/doi.org\/10.1109\/jiot.2021.3121517.","journal-title":"IEEE Internet Things J"},{"key":"308_CR4","doi-asserted-by":"publisher","unstructured":"Awais A, Iqbal Z. Network Traffic Classification through Machine Learning methods in IoT Networks. 2022 International Conference on IT and Industrial Technologies (ICIT) 2022:01\u20136. https:\/\/doi.org\/10.1109\/icit56493.2022.9989079","DOI":"10.1109\/icit56493.2022.9989079"},{"key":"308_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-19-2879-6","volume-title":"Artificial Intelligence Technology","year":"2023","unstructured":"Lv Y, Wang L, Gong X, Chen M, editors. Artificial Intelligence Technology. Singapore: Springer Nature Singapore; 2023. https:\/\/doi.org\/10.1007\/978-981-19-2879-6."},{"key":"308_CR6","doi-asserted-by":"publisher","unstructured":"Ferreira FRT, Couto LMD. Using deep learning on microscopic images for white blood cell detection and segmentation to assist in leukemia diagnosis. J Supercomputing. 2025;81. https:\/\/doi.org\/10.1007\/s11227-024-06903-2.","DOI":"10.1007\/s11227-024-06903-2"},{"key":"308_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-025-10906-3","author":"FRT Ferreira","year":"2025","unstructured":"Ferreira FRT, Couto LMD, Saporetti CM. Using deep learning applied in computer vision for the inclusion of individuals with tetraplegia through assistive robotics via facial features. Soft Comput. 2025. https:\/\/doi.org\/10.1007\/s00500-025-10906-3.","journal-title":"Soft Comput"},{"key":"308_CR8","doi-asserted-by":"publisher","unstructured":"Ferreira FRT. Couto,Domingues,: Comparing the efficiency of YOLO-M for face recognition in images and videos degraded by compression artifacts.Evolving Systems(2025). https:\/\/doi.org\/10.1007\/S12530-025-09699-5","DOI":"10.1007\/S12530-025-09699-5"},{"key":"308_CR9","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/s10772-024-10108-6","volume":"27","author":"MH Alsalihi","year":"2024","unstructured":"Alsalihi MH, Sztah\u00f3 D. Effect of identical twins on deep speaker embeddings based forensic voice comparison. Int J Speech Technol. 2024;27:341\u201351. https:\/\/doi.org\/10.1007\/s10772-024-10108-6.","journal-title":"Int J Speech Technol"},{"key":"308_CR10","doi-asserted-by":"publisher","first-page":"7462","DOI":"10.1109\/tits.2022.3159092","volume":"24","author":"S Otoum","year":"2022","unstructured":"Otoum S, Guizani N, Mouftah H. On the Feasibility of Split Learning, Transfer Learning and Federated Learning for Preserving Security in ITS Systems. IEEE Trans Intell Transp Syst. 2022;24:7462\u201370. https:\/\/doi.org\/10.1109\/tits.2022.3159092.","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"308_CR11","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.1007\/s11042-022-13248-6","volume":"82","author":"MK Rusia","year":"2022","unstructured":"Rusia MK, Singh DK. A comprehensive survey on techniques to handle face identity threats: challenges and opportunities. Multimedia Tools Appl. 2022;82:1669\u2013748. https:\/\/doi.org\/10.1007\/s11042-022-13248-6.","journal-title":"Multimedia Tools Appl"},{"key":"308_CR12","doi-asserted-by":"publisher","first-page":"6438","DOI":"10.1109\/jiot.2023.3310794","volume":"11","author":"S Zhu","year":"2023","unstructured":"Zhu S, Xu X, Zhao J, Xiao F. LKD-STNN: a lightweight malicious traffic detection method for internet of things based on knowledge distillation. IEEE Internet Things J. 2023;11:6438\u201353. https:\/\/doi.org\/10.1109\/jiot.2023.3310794.","journal-title":"IEEE Internet Things J"},{"key":"308_CR13","doi-asserted-by":"publisher","unstructured":"Ferreira FRT, Couto LMD. Development of a computational deep learning model for detecting people in aerial images and videos degraded by compression artifacts. Earth Sci Inf. 2025;18. https:\/\/doi.org\/10.1007\/s12145-025-01916-8.","DOI":"10.1007\/s12145-025-01916-8"},{"key":"308_CR14","doi-asserted-by":"publisher","unstructured":"Alhindi A, Al-Ahmadi S, Ismail MMB. Balancing Privacy and Utility in Split Learning: An Adversarial Channel Pruning-Based approach. IEEE Access. 2025;1. https:\/\/doi.org\/10.1109\/access.2025.3528575.","DOI":"10.1109\/access.2025.3528575"},{"key":"308_CR15","doi-asserted-by":"publisher","DOI":"10.1145\/3687478","author":"Y Jia","year":"2024","unstructured":"Jia Y, Liu B, Zhang X, Dai F, Khan A, Qi L, et al. Model pruning-enabled federated split learning for resource-constrained devices in artificial intelligence empowered edge computing environment. ACM Trans Sens Networks. 2024. https:\/\/doi.org\/10.1145\/3687478.","journal-title":"ACM Trans Sens Networks"},{"key":"308_CR16","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1109\/tcad.2018.2819366","volume":"38","author":"N Rathi","year":"2018","unstructured":"Rathi N, Panda P, Roy K. STDP-Based pruning of connections and weight quantization in spiking neural networks for Energy-Efficient Recognition. IEEE Trans Comput Aided Des Integr Circuits Syst. 2018;38:668\u201377. https:\/\/doi.org\/10.1109\/tcad.2018.2819366.","journal-title":"IEEE Trans Comput Aided Des Integr Circuits Syst"},{"key":"308_CR17","doi-asserted-by":"publisher","unstructured":"Addas A, Khan MN, Tahir M, Naseer F, Gulzar Y, Onn CW. Integrating sensor data and GAN-based models to optimize medical university distribution: a data-driven approach for sustainable regional growth in Saudi Arabia. Front Educ. 2025;10. https:\/\/doi.org\/10.3389\/feduc.2025.1527337.","DOI":"10.3389\/feduc.2025.1527337"},{"key":"308_CR18","doi-asserted-by":"publisher","first-page":"1520592","DOI":"10.3389\/frai.2025.1520592","volume":"8","author":"F Naseer","year":"2025","unstructured":"Naseer F, Addas A, Tahir M, Khan MN, Sattar N. Integrating generative adversarial networks with IoT for adaptive AI-powered personalized elderly care in smart homes. Front Artif Intell. 2025;8:1520592. https:\/\/doi.org\/10.3389\/frai.2025.1520592.","journal-title":"Front Artif Intell"},{"key":"308_CR19","doi-asserted-by":"publisher","first-page":"5827","DOI":"10.1109\/jiot.2019.2952146","volume":"7","author":"PCM Arachchige","year":"2019","unstructured":"Arachchige PCM, Bertok P, Khalil I, Liu D, Camtepe S, Atiquzzaman M. Local differential privacy for deep learning. IEEE Internet Things J. 2019;7:5827\u201342. https:\/\/doi.org\/10.1109\/jiot.2019.2952146.","journal-title":"IEEE Internet Things J"},{"key":"308_CR20","doi-asserted-by":"publisher","unstructured":"Mukesh N, Kumar MR, Vikas AS, Jeyakumar G. An Empirical Comparative Study on Pruning and Quantization Algorithms for Model Compression, International Conference on Machine Learning and Autonomous Systems (ICMLAS-2025), pp. 1036\u20131043. https:\/\/doi.org\/10.1109\/icmlas64557.2025.10967650","DOI":"10.1109\/icmlas64557.2025.10967650"},{"key":"308_CR21","doi-asserted-by":"publisher","first-page":"109380","DOI":"10.1016\/j.comnet.2022.109380","volume":"218","author":"E Samikwa","year":"2022","unstructured":"Samikwa E, Di Maio A, Braun T. ARES: Adaptive Resource-Aware Split Learning for Internet of Things. Comput Netw. 2022;218:109380. https:\/\/doi.org\/10.1016\/j.comnet.2022.109380.","journal-title":"Comput Netw"},{"key":"308_CR22","doi-asserted-by":"publisher","first-page":"4197","DOI":"10.1109\/tce.2024.3367330","volume":"70","author":"D-J Kim","year":"2024","unstructured":"Kim D-J, Amma NGB, Sarveshwaran V. A novel Split Learning-Based Consumer Electronics Network Traffic Anomaly Detection Framework for Smart City Environment. IEEE Trans Consum Electron. 2024;70:4197\u2013204. https:\/\/doi.org\/10.1109\/tce.2024.3367330.","journal-title":"IEEE Trans Consum Electron"},{"key":"308_CR23","doi-asserted-by":"publisher","first-page":"2881","DOI":"10.1109\/tifs.2024.3356821","volume":"19","author":"J Liu","year":"2024","unstructured":"Liu J, Lyu X, Cui Q, Tao X. Similarity-Based label inference attack against training and inference of split learning. IEEE Trans Inf Forensics Secur. 2024;19:2881\u201395. https:\/\/doi.org\/10.1109\/tifs.2024.3356821.","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"308_CR24","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/mwc.014.2200438","volume":"31","author":"X Lyu","year":"2023","unstructured":"Lyu X, Liu J, Ren C, Nan G. Security-Communication-Computation tradeoff of split decisions for edge intelligence. IEEE Wirel Commun. 2023;31:257\u201363. https:\/\/doi.org\/10.1109\/mwc.014.2200438.","journal-title":"IEEE Wirel Commun"},{"key":"308_CR25","doi-asserted-by":"publisher","first-page":"66680","DOI":"10.1109\/access.2024.3399541","volume":"12","author":"D Tian","year":"2024","unstructured":"Tian D, Yamagiwa S, Wada K. Heuristic compression method for CNN model applying quantization to a combination of structured and unstructured pruning techniques. IEEE Access. 2024;12:66680\u20139. https:\/\/doi.org\/10.1109\/access.2024.3399541.","journal-title":"IEEE Access"},{"key":"308_CR26","doi-asserted-by":"publisher","first-page":"88","DOI":"10.3390\/s24010088","volume":"24","author":"H Chen","year":"2023","unstructured":"Chen H, Chen X, Peng L, Bai Y. Personalized fair split learning for Resource-Constrained internet of things. Sensors. 2023;24:88. https:\/\/doi.org\/10.3390\/s24010088.","journal-title":"Sensors"},{"key":"308_CR27","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1109\/tetci.2023.3341299","volume":"8","author":"X Wang","year":"2023","unstructured":"Wang X, Fan W, Hu X, He J, Chi C-H. Differential Privacy-Preserving of Multi-Party collaboration under federated learning in data center networks. IEEE Trans Emerg Top Comput Intell. 2023;8:1223\u201337. https:\/\/doi.org\/10.1109\/tetci.2023.3341299.","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"key":"308_CR28","doi-asserted-by":"publisher","unstructured":"Ferreira FRT, Couto LMD, De Melo Baptista Domingues G, Saporetti CM. Development of a framework using deep learning for the identification and classification of engagement levels in distance learning students. Social Netw Anal Min. 2025;15. https:\/\/doi.org\/10.1007\/s13278-025-01408-z.","DOI":"10.1007\/s13278-025-01408-z"},{"key":"308_CR29","doi-asserted-by":"publisher","first-page":"1857","DOI":"10.1007\/s00146-023-01634-z","volume":"39","author":"VL Raposo","year":"2023","unstructured":"Raposo VL. When facial recognition does not \u2018recognise\u2019: erroneous identifications and resulting liabilities. AI Soc. 2023;39:1857\u201369. https:\/\/doi.org\/10.1007\/s00146-023-01634-z.","journal-title":"AI Soc"},{"key":"308_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-10164-8","author":"A Apap","year":"2024","unstructured":"Apap A, Bhole A, Fern\u00e1ndez-Robles L, Castej\u00f3n-Limas M, Azzopardi G. Explainable multi-layer COSFIRE filters robust to corruptions and boundary attack with application to retina and palmprint biometrics. Neural Comput Appl. 2024. https:\/\/doi.org\/10.1007\/s00521-024-10164-8.","journal-title":"Neural Comput Appl"},{"key":"308_CR31","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/mprv.2018.03367731","volume":"17","author":"Y Meidan","year":"2018","unstructured":"Meidan Y, Bohadana M, Mathov Y, Mirsky Y, Shabtai A, Breitenbacher D, et al. N-BAIOT\u2014Network-Based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Comput. 2018;17:12\u201322. https:\/\/doi.org\/10.1109\/mprv.2018.03367731.","journal-title":"IEEE Pervasive Comput"},{"key":"308_CR32","unstructured":"Pruning comprehensive guide. TensorFlow n.d. https:\/\/www.tensorflow.org\/model_optimization\/guide\/pruning\/comprehensive_guide"},{"key":"308_CR33","doi-asserted-by":"publisher","unstructured":"Waqdan M, Louafi H, Mouhoub M. Security risk assessment in IoT environments: A taxonomy and survey. Computers Secur. 2025;104456. https:\/\/doi.org\/10.1016\/j.cose.2025.104456.","DOI":"10.1016\/j.cose.2025.104456"},{"key":"308_CR34","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1016\/j.jiixd.2023.12.001","volume":"2","author":"T Sasi","year":"2023","unstructured":"Sasi T, Lashkari AH, Lu R, Xiong P, Iqbal S. A comprehensive survey on IoT attacks: Taxonomy, detection mechanisms and challenges. J Inform Intell. 2023;2:455\u2013513. https:\/\/doi.org\/10.1016\/j.jiixd.2023.12.001.","journal-title":"J Inform Intell"},{"key":"308_CR35","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.2403.10968","author":"G Shirvani","year":"2024","unstructured":"Shirvani G, Ghasemshirazi S, Alipour MA. Enhancing IoT Security Against DDoS Attacks through Federated Learning. arXiv (Cornell University). 2024. https:\/\/doi.org\/10.48550\/arxiv.2403.10968.","journal-title":"arXiv (Cornell University)"},{"key":"308_CR36","doi-asserted-by":"publisher","first-page":"3911","DOI":"10.3390\/electronics12183911","volume":"12","author":"Y Yang","year":"2023","unstructured":"Yang Y, Gu Y, Yan Y. Machine Learning-Based Intrusion Detection for Rare-Class Network Attacks. Electronics. 2023;12:3911. https:\/\/doi.org\/10.3390\/electronics12183911.","journal-title":"Electronics"},{"key":"308_CR37","doi-asserted-by":"publisher","first-page":"167168","DOI":"10.1109\/access.2024.3495702","volume":"12","author":"R Dhakal","year":"2024","unstructured":"Dhakal R, Raza W, Tummala V, Kandel LN. Enhancing intrusion detection in IoT networks through federated learning. IEEE Access. 2024;12:167168\u201382. https:\/\/doi.org\/10.1109\/access.2024.3495702.","journal-title":"IEEE Access"}],"container-title":["Discover Internet of Things"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s43926-026-00308-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43926-026-00308-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43926-026-00308-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T12:01:35Z","timestamp":1774353695000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s43926-026-00308-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,16]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["308"],"URL":"https:\/\/doi.org\/10.1007\/s43926-026-00308-8","relation":{},"ISSN":["2730-7239"],"issn-type":[{"value":"2730-7239","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,16]]},"assertion":[{"value":"23 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"36"}}