{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:09:24Z","timestamp":1781222964783,"version":"3.54.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comput Virol Hack Tech"],"DOI":"10.1007\/s11416-025-00582-0","type":"journal-article","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T16:53:22Z","timestamp":1761152002000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Adaptive Federated Edge Intelligence for Real-Time Cyberthreat Detection in Resource-Constrained IoT Environments: A Lightweight Deep Learning Approach"],"prefix":"10.1007","volume":"21","author":[{"given":"Milad","family":"Rahmati","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nima","family":"Rahmati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,22]]},"reference":[{"key":"582_CR1","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.comnet.2014.11.008","volume":"76","author":"S Sicari","year":"2015","unstructured":"Sicari, S., Rizzardi, A., Grieco, L.A., Coen-Porisini, A.: Security, privacy and trust in Internet of Things: The road ahead. Comput. Netw. 76, 146\u2013164 (2015). https:\/\/doi.org\/10.1016\/j.comnet.2014.11.008","journal-title":"Comput. Netw."},{"issue":"5","key":"582_CR2","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1109\/JIOT.2017.2683200","volume":"4","author":"J Lin","year":"2017","unstructured":"Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., Zhao, W.: A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4(5), 1125\u20131142 (2017). https:\/\/doi.org\/10.1109\/JIOT.2017.2683200","journal-title":"IEEE Internet Things J."},{"issue":"5","key":"582_CR3","doi-asserted-by":"publisher","first-page":"1250","DOI":"10.1109\/JIOT.2017.2694844","volume":"4","author":"Y Yang","year":"2017","unstructured":"Yang, Y., Wu, L., Yin, G., Li, L., Zhao, H.: A survey on security and privacy issues in Internet-of-Things. IEEE Internet Things J. 4(5), 1250\u20131258 (2017). https:\/\/doi.org\/10.1109\/JIOT.2017.2694844","journal-title":"IEEE Internet Things J."},{"issue":"4","key":"582_CR4","doi-asserted-by":"publisher","first-page":"2483","DOI":"10.1109\/JIOT.2017.2767291","volume":"5","author":"M Frustaci","year":"2018","unstructured":"Frustaci, M., Pace, P., Aloi, G., Fortino, G.: Evaluating critical security issues of the IoT world: Present and future challenges. IEEE Internet Things J. 5(4), 2483\u20132495 (2018). https:\/\/doi.org\/10.1109\/JIOT.2017.2767291","journal-title":"IEEE Internet Things J."},{"key":"582_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2019.100129","author":"H HaddadPajouh","year":"2021","unstructured":"HaddadPajouh, H., Dehghantanha, A., Parizi, R.M., Aledhari, M., Karimipour, H.: A survey on internet of things security: Requirements, challenges, and solutions. Internet of Things (2021). https:\/\/doi.org\/10.1016\/j.iot.2019.100129","journal-title":"Internet of Things"},{"issue":"4","key":"582_CR6","doi-asserted-by":"publisher","first-page":"2347","DOI":"10.1109\/COMST.2015.2444095","volume":"17","author":"A Al-Fuqaha","year":"2015","unstructured":"Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17(4), 2347\u20132376 (2015). https:\/\/doi.org\/10.1109\/COMST.2015.2444095","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"5","key":"582_CR7","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","volume":"3","author":"W Shi","year":"2016","unstructured":"Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: Vision and challenges. IEEE Internet Things J. 3(5), 637\u2013646 (2016). https:\/\/doi.org\/10.1109\/JIOT.2016.2579198","journal-title":"IEEE Internet Things J."},{"issue":"5","key":"582_CR8","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1109\/MNET.2019.1800286","volume":"33","author":"X Wang","year":"2019","unstructured":"Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., Chen, M.: In-edge AI: Intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Netw. 33(5), 156\u2013165 (2019). https:\/\/doi.org\/10.1109\/MNET.2019.1800286","journal-title":"IEEE Netw."},{"key":"582_CR9","doi-asserted-by":"publisher","unstructured":"Konechy, J., McMahan, H.B., Yu, F.X., Richt\u00e1rik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency, arXiv preprint arXiv:1610.05492, Oct. (2016). https:\/\/doi.org\/10.48550\/arXiv.1610.05492","DOI":"10.48550\/arXiv.1610.05492"},{"issue":"3","key":"582_CR10","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: Challenges, methods, and future directions. IEEE. Signal. Process. Mag. 37(3), 50\u201360 (2020). https:\/\/doi.org\/10.1109\/MSP.2020.2975749","journal-title":"IEEE. Signal. Process. Mag."},{"key":"582_CR11","doi-asserted-by":"publisher","first-page":"8956","DOI":"10.1109\/ACCESS.2017.2695525","volume":"5","author":"L Chen","year":"2017","unstructured":"Chen, L., Thombre, S., J\u00e4rvinen, K., Lohan, E.S., Al\u00e9n-Savikko, A., Lepp\u00e4koski, H., Bhuiyan, M.Z.H., Bu, S., Talvitie, J., Ferrara, P., Kuusniemi, H.: Robustness, security and privacy in location-based services for future IoT: A survey. IEEE Access. 5, 8956\u20138977 (2017). https:\/\/doi.org\/10.1109\/ACCESS.2017.2695525","journal-title":"IEEE Access."},{"key":"582_CR12","doi-asserted-by":"publisher","unstructured":"Kumar, P., Singh, R.: Detection of malware in android mobile using permission-based machine learning, in Proc. International Conference on Computing, Communication and Automation (ICCCA), Noida, India, Apr. pp. 338\u2013343, (2020). https:\/\/doi.org\/10.1109\/ICCCA49541.2020.9250790","DOI":"10.1109\/ICCCA49541.2020.9250790"},{"key":"582_CR13","doi-asserted-by":"publisher","unstructured":"Alrashdi, I., Alqazzaz, A., Aloufi, E., Alharthi, R., Zohdy, M., Ming, H.: Ad-iot: Anomaly detection of iot cyberattacks in smart city using machine learning, in Proc. IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, Jan. pp. 305\u2013310, (2019). https:\/\/doi.org\/10.1109\/CCWC.2019.8666450","DOI":"10.1109\/CCWC.2019.8666450"},{"key":"582_CR14","doi-asserted-by":"publisher","unstructured":"Wang, Y., Uehara, T., Sasaki, R.: Fog computing: Issues and challenges in security and forensics, in Proc. IEEE 39th Annual Computer Software and Applications Conference, Taichung, Taiwan, Jul. pp. 53\u201359, (2015). https:\/\/doi.org\/10.1109\/COMPSAC.2015.173","DOI":"10.1109\/COMPSAC.2015.173"},{"key":"582_CR15","doi-asserted-by":"publisher","unstructured":"Nguyen, A., Reddi, S.: Adversarial examples in the physical world, arXiv preprint arXiv:1607.02533, Jul. (2016). https:\/\/doi.org\/10.48550\/arXiv.1607.02533","DOI":"10.48550\/arXiv.1607.02533"},{"key":"582_CR16","doi-asserted-by":"publisher","first-page":"157463","DOI":"10.1109\/ACCESS.2020.3019827","volume":"8","author":"J Li","year":"2020","unstructured":"Li, J., Zhao, D., BashirShaban, B.F., Xie, W., Li, H., Xu, M., Wang, Q.: A task-oriented chatbot based on transformer architecture in healthcare domain. IEEE Access. 8, 157463\u2013157474 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3019827","journal-title":"IEEE Access."},{"issue":"12","key":"582_CR17","doi-asserted-by":"publisher","first-page":"9320","DOI":"10.1109\/JIOT.2021.3056918","volume":"8","author":"S Zhang","year":"2021","unstructured":"Zhang, S., Liu, D.: A novel edge intelligence framework for IoT anomaly detection. IEEE Internet Things J. 8(12), 9320\u20139332 (2021). https:\/\/doi.org\/10.1109\/JIOT.2021.3056918","journal-title":"IEEE Internet Things J."},{"issue":"4","key":"582_CR18","doi-asserted-by":"publisher","first-page":"3191","DOI":"10.1109\/JIOT.2020.2964351","volume":"7","author":"K Patel","year":"2020","unstructured":"Patel, K., Mehta, S., Lolla, D., Volchenkov, V.: IoT-enabled edge computing framework for indoor air quality monitoring and predictive maintenance. IEEE Internet Things J. 7(4), 3191\u20133200 (2020). https:\/\/doi.org\/10.1109\/JIOT.2020.2964351","journal-title":"IEEE Internet Things J."},{"issue":"4","key":"582_CR19","doi-asserted-by":"publisher","first-page":"2261","DOI":"10.1109\/COMST.2020.3013488","volume":"22","author":"R Thompson","year":"2020","unstructured":"Thompson, R., Chen, M., Wang, L., Kumar, S.: Resource-aware edge computing for IoT: A comprehensive survey. IEEE Commun. Surv. Tutor. 22(4), 2261\u20132295 (2020). https:\/\/doi.org\/10.1109\/COMST.2020.3013488","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"582_CR20","doi-asserted-by":"publisher","first-page":"1478","DOI":"10.1109\/TIFS.2020.3047894","volume":"16","author":"A Martinez","year":"2021","unstructured":"Martinez, A., Brown, J.: Lightweight neural network compression for edge security applications. IEEE Trans. Inf. Forensics Secur. 16, 1478\u20131492 (2021). https:\/\/doi.org\/10.1109\/TIFS.2020.3047894","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"582_CR21","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data, in Proc. 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, FL, USA, Apr. pp. 1273\u20131282. (2017)"},{"key":"582_CR22","doi-asserted-by":"publisher","unstructured":"Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., Ramage, D.: Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811 03604. (Nov. 2018). https:\/\/doi.org\/10.48550\/arXiv.1811.03604","DOI":"10.48550\/arXiv.1811.03604"},{"key":"582_CR23","doi-asserted-by":"publisher","unstructured":"Kone\u010dn\u00fd, J., McMahan, H.B., Ramage, D., Richt\u00e1rik, P.: Federated optimization: Distributed machine learning for on-device intelligence, arXiv preprint arXiv:1610.02527, Oct. (2016). https:\/\/doi.org\/10.48550\/arXiv.1610.02527","DOI":"10.48550\/arXiv.1610.02527"},{"key":"582_CR24","doi-asserted-by":"publisher","unstructured":"Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-iid data, arXiv preprint arXiv:1806.00582, Jun. (2018). https:\/\/doi.org\/10.48550\/arXiv.1806.00582","DOI":"10.48550\/arXiv.1806.00582"},{"key":"582_CR25","doi-asserted-by":"publisher","unstructured":"Liu, L., Zhang, J., Song, S., Letaief, K.B.: Client-edge-cloud hierarchical federated learning, in Proc. IEEE International Conference on Communications (ICC), Dublin, Ireland, Jun. pp. 1\u20136, (2020). https:\/\/doi.org\/10.1109\/ICC40277.2020.9148862","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"582_CR26","doi-asserted-by":"publisher","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv:1704.04861, Apr. (2017). https:\/\/doi.org\/10.48550\/arXiv.1704.04861","DOI":"10.48550\/arXiv.1704.04861"},{"key":"582_CR27","doi-asserted-by":"publisher","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: Inverted residuals and linear bottlenecks, in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, Jun. pp. 4510\u20134520, (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00474","DOI":"10.1109\/CVPR.2018.00474"},{"key":"582_CR28","unstructured":"Tan, M., Le, Q.: EfficientNet: Rethinking model scaling for convolutional neural networks, in Proc. 36th International Conference on Machine Learning (ICML), Long Beach, CA, USA, Jun. pp. 6105\u20136114. (2019)"},{"key":"582_CR29","doi-asserted-by":"publisher","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, arXiv preprint arXiv:1510.00149, Oct. (2015). https:\/\/doi.org\/10.48550\/arXiv.1510.00149","DOI":"10.48550\/arXiv.1510.00149"},{"key":"582_CR30","doi-asserted-by":"publisher","unstructured":"Dwork, C., Roth, A.: The algorithmic foundations of differential privacy, Foundations and Trends in Theoretical Computer Science, vol. 9, no. 3\u20134, pp. 211\u2013407, Aug. (2014). https:\/\/doi.org\/10.1561\/0400000042","DOI":"10.1561\/0400000042"},{"key":"582_CR31","doi-asserted-by":"publisher","unstructured":"Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., Seth, K., Practical secure aggregation for privacy-preserving machine learning, in Proc. ACM SIGSAC Conference on Computer and, Security, C.: Dallas, TX, USA, Oct. pp. 1175\u20131191, (2017). https:\/\/doi.org\/10.1145\/3133956.3133982","DOI":"10.1145\/3133956.3133982"},{"key":"582_CR32","doi-asserted-by":"publisher","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate, arXiv preprint arXiv:1409.0473, Sep. (2014). https:\/\/doi.org\/10.48550\/arXiv.1409.0473","DOI":"10.48550\/arXiv.1409.0473"},{"key":"582_CR33","unstructured":"Garcia, S., Parmisano, A., Erquiaga, M.J.: IoT-23: A labeled dataset with malicious and benign IoT network traffic, Jan. [Online]. (2020). Available: https:\/\/www.stratosphereips.org\/datasets-iot23"},{"key":"582_CR34","doi-asserted-by":"publisher","unstructured":"Moustafa, N., Slay, J., UNSW-NB15: Nov., : A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set), in Proc. Military Communications and Information Systems Conference (MilCIS), Canberra, Australia, pp. 1\u20136, (2015). https:\/\/doi.org\/10.1109\/MilCIS.2015.7348942","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"582_CR35","doi-asserted-by":"publisher","unstructured":"Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization, in Proc. 4th International Conference on Information Systems Security and Privacy (ICISSP), Funchal, Portugal, Jan. pp. 108\u2013116, (2018). https:\/\/doi.org\/10.5220\/0006639801080116","DOI":"10.5220\/0006639801080116"},{"key":"582_CR36","doi-asserted-by":"publisher","first-page":"10629","DOI":"10.1109\/JIOT.2021.3127734","volume":"9","author":"P Zhang","year":"2022","unstructured":"Zhang, P., et al.: Federated learning for Internet of Things: Applications, challenges, and opportunities. IEEE Internet Things J. 9, 10629\u201310642 (2022). https:\/\/doi.org\/10.1109\/JIOT.2021.3127734","journal-title":"IEEE Internet Things J."},{"issue":"1","key":"582_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JIOT.2021.3095077","volume":"9","author":"A Imteaj","year":"2022","unstructured":"Imteaj, A., et al.: A survey on federated learning for resource-constrained IoT devices. IEEE Internet Things J. 9(1), 1\u201324 (2022). https:\/\/doi.org\/10.1109\/JIOT.2021.3095077","journal-title":"IEEE Internet Things J."},{"key":"582_CR38","doi-asserted-by":"publisher","first-page":"103496","DOI":"10.1016\/j.cose.2023.103496","volume":"135","author":"M Asad","year":"2023","unstructured":"Asad, M., et al.: Federated learning for IoT\/Edge computing: A state-of-the-art survey. Computers Secur. 135, 103496 (2023). https:\/\/doi.org\/10.1016\/j.cose.2023.103496","journal-title":"Computers Secur."},{"issue":"7","key":"582_CR39","doi-asserted-by":"publisher","first-page":"5476","DOI":"10.1109\/JIOT.2020.3030072","volume":"8","author":"S Abdulrahman","year":"2021","unstructured":"Abdulrahman, S., et al.: A survey on federated learning: The journey from centralized to distributed on-site learning and beyond. IEEE Internet Things J. 8(7), 5476\u20135497 (2021). https:\/\/doi.org\/10.1109\/JIOT.2020.3030072","journal-title":"IEEE Internet Things J."},{"issue":"4","key":"582_CR40","doi-asserted-by":"publisher","first-page":"4158","DOI":"10.1109\/TNSM.2022.3200023","volume":"19","author":"Y Lu","year":"2022","unstructured":"Lu, Y., et al.: Lightweight deep learning for IoT intrusion detection. IEEE Trans. Netw. Serv. Manage. 19(4), 4158\u20134171 (2022). https:\/\/doi.org\/10.1109\/TNSM.2022.3200023","journal-title":"IEEE Trans. Netw. Serv. Manage."},{"issue":"5","key":"582_CR41","doi-asserted-by":"publisher","first-page":"4567","DOI":"10.1109\/TMC.2023.3278901","volume":"23","author":"Z Zhang","year":"2024","unstructured":"Zhang, Z., et al.: Adaptive federated learning for edge intelligence in IoT security. IEEE Trans. Mob. Comput. 23(5), 4567\u20134581 (2024). https:\/\/doi.org\/10.1109\/TMC.2023.3278901","journal-title":"IEEE Trans. Mob. Comput."},{"key":"582_CR42","doi-asserted-by":"publisher","unstructured":"Mills, J., et al.: Federated learning challenges and opportunities: An outlook, in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8752\u20138756, (2022). https:\/\/doi.org\/10.1109\/ICASSP43922.2022.9746291","DOI":"10.1109\/ICASSP43922.2022.9746291"},{"issue":"8","key":"582_CR43","doi-asserted-by":"publisher","first-page":"3987","DOI":"10.3390\/s23083987","volume":"23","author":"H Yang","year":"2023","unstructured":"Yang, H., et al.: Lightweight convolutional neural network for IoT anomaly detection. Sensors 23(8), 3987 (2023). https:\/\/doi.org\/10.3390\/s23083987","journal-title":"Sensors"},{"issue":"4","key":"582_CR44","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.1109\/JIOT.2021.3075433","volume":"9","author":"S Otoum","year":"2022","unstructured":"Otoum, S., et al.: Federated learning-based anomaly detection for IoT security attacks. IEEE Internet Things J. 9(4), 2545\u20132554 (2022). https:\/\/doi.org\/10.1109\/JIOT.2021.3075433","journal-title":"IEEE Internet Things J."},{"key":"582_CR45","doi-asserted-by":"publisher","first-page":"7845","DOI":"10.1109\/TVT.2023.3240912","volume":"72","author":"B Li","year":"2023","unstructured":"Li, B., et al.: Privacy-preserving federated learning for UAV-enabled networks: Learning-based adaptive controller. IEEE Trans. Veh. Technol. 72, 7845\u20137858 (2023). https:\/\/doi.org\/10.1109\/TVT.2023.3240912","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"2","key":"582_CR46","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1109\/TBDATA.2023.3321789","volume":"10","author":"M Chen","year":"2024","unstructured":"Chen, M., et al.: A survey of federated learning with label noise. IEEE Trans. Big Data. 10(2), 184\u2013199 (2024). https:\/\/doi.org\/10.1109\/TBDATA.2023.3321789","journal-title":"IEEE Trans. Big Data"},{"issue":"15","key":"582_CR47","doi-asserted-by":"publisher","first-page":"13456","DOI":"10.1109\/JIOT.2023.3260789","volume":"10","author":"X Yuan","year":"2023","unstructured":"Yuan, X., et al.: TinyFedTL: Federated transfer learning for resource-constrained IoT devices. IEEE Internet Things J. 10(15), 13456\u201313468 (2023). https:\/\/doi.org\/10.1109\/JIOT.2023.3260789","journal-title":"IEEE Internet Things J."},{"key":"582_CR48","doi-asserted-by":"crossref","unstructured":"Sattler, F., et al.: Robust and communication-efficient federated learning from non-i.i.d. data, IEEE Trans. Neural Netw. Learn. Syst. 31(9), 3400\u20133413 (2020), doi: 10.1109\/TNNLS.2019.2944481. (Borderline recent, but relevant for adaptation.)","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"582_CR49","doi-asserted-by":"publisher","unstructured":"Zhang, L., et al.: Adaptive aggregation for federated learning in IoT, in Proc. IEEE INFOCOM, pp. 1\u201310, (2023). https:\/\/doi.org\/10.1109\/INFOCOM53939.2023.10229012","DOI":"10.1109\/INFOCOM53939.2023.10229012"},{"issue":"2","key":"582_CR50","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1109\/TCCN.2022.3152900","volume":"8","author":"A Taik","year":"2022","unstructured":"Taik, A., et al.: Data-aware device scheduling for federated edge learning. IEEE Trans. Cogn. Commun. Netw. 8(2), 587\u2013602 (2022). https:\/\/doi.org\/10.1109\/TCCN.2022.3152900","journal-title":"IEEE Trans. Cogn. Commun. Netw."}],"container-title":["Journal of Computer Virology and Hacking Techniques"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11416-025-00582-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11416-025-00582-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11416-025-00582-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T17:20:31Z","timestamp":1764004831000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11416-025-00582-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,22]]},"references-count":50,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["582"],"URL":"https:\/\/doi.org\/10.1007\/s11416-025-00582-0","relation":{},"ISSN":["2263-8733"],"issn-type":[{"value":"2263-8733","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,22]]},"assertion":[{"value":"17 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"35"}}