{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:42:22Z","timestamp":1760060542785,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T00:00:00Z","timestamp":1756512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Inner Mongolia Autonomous Region of China","award":["2025MS06005"],"award-info":[{"award-number":["2025MS06005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Federated learning enables collaborative model training across distributed devices while preserving data privacy. However, in real-world environments such as smart cities, heterogeneous and resource-constrained edge devices often render existing methods impractical. Low-power sensors and cameras struggle to complete full-model training, while high-performance devices remain idly waiting for others. Knowledge distillation approaches rely on public datasets that are rarely available or poorly aligned with urban data, which limits their effectiveness in deployment. These limitations lead to inefficiencies, unstable convergence, and poor adaptability in diverse urban networks. Partial training alleviates some challenges by allowing clients to train submodels tailored to their capacity, but existing methods still incur high computational costs for identifying important parameters and suffer from uneven parameter updates, reducing model effectiveness. To address these challenges, we propose Parameter-Level Dynamic Submodel Extraction (PLDSE), a lightweight and adaptive framework for federated learning. PLDSE estimates parameter importance using gradient-based scores on a server-side validation set, reducing overhead while accurately identifying critical parameters. In addition, it integrates a rolling scheduling mechanism to rotate unselected parameters, ensuring full coverage and consistent model updates. Experiments on CIFAR-10, CIFAR-100, and Fashion-MNIST demonstrate superior accuracy and faster convergence, with PLDSE achieving 62.82% on CIFAR-100 under low heterogeneity and 61.51% under high heterogeneity, outperforming prior methods.<\/jats:p>","DOI":"10.3390\/bdcc9090226","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T14:02:46Z","timestamp":1756735366000},"page":"226","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FedPLDSE: Submodel Extraction for Federated Learning in Heterogeneous Smart City Devices"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0751-6957","authenticated-orcid":false,"given":"Xiaochi","family":"Hou","sequence":"first","affiliation":[{"name":"College of Computer Science, Inner Mongolia University, Hohhot 010021, China"}]},{"given":"Zhigang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Inner Mongolia University, Hohhot 010021, China"}]},{"given":"Xinhao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Inner Mongolia University, Hohhot 010021, China"}]},{"given":"Junfeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer Science, Inner Mongolia University, Hohhot 010021, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3411843","article-title":"Heartquake: Accurate low-cost non-invasive ECG monitoring using bed-mounted geophones","volume":"4","author":"Park","year":"2020","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Leroy, D., Coucke, A., Lavril, T., Gisselbrecht, T., and Dureau, J. (2019, January 12\u201317). Federated learning for keyword spotting. Proceedings of the ICASSP 2019\u2014IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683546"},{"key":"ref_3","unstructured":"Jiang, W., Miao, C., Ma, F., Yao, S., Wang, Y., Yuan, Y., Xue, H., Song, C., Ma, X., and Koutsonikolas, D. (November, January 29). Towards environment independent device free human activity recognition. Proceedings of the 24th Annual International Conference on Mobile Computing and Networking (MobiCom), New Delhi, India."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Balfaqih, M., Balfagih, Z., Lytras, M.D., Alfawaz, K.M., Alshdadi, A.A., and Alsolami, E. (2023). A blockchain-enabled IoT logistics system for efficient tracking and management of high-price shipments: A resilient, scalable and sustainable approach to smart cities. Sustainability, 15.","DOI":"10.3390\/su151813971"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, Y., Ning, Y., Slawski, M., and Rangwala, H. (2020, January 10\u201313). Asynchronous online federated learning for edge devices with non-iid data. Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA.","DOI":"10.1109\/BigData50022.2020.9378161"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Balfaqih, M., and Alharbi, S.A. (2022). Associated information and communication technologies challenges of smart city development. Sustainability, 14.","DOI":"10.3390\/su142316240"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000083","article-title":"Advances and open problems in federated learning","volume":"14","author":"Kairouz","year":"2021","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_8","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and Arcas, B.A.y. (2017, January 20\u201322). Communication-efficient learning of deep networks from decentralized data. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, FL, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"53841","DOI":"10.1109\/ACCESS.2020.2981430","article-title":"Federated learning for UAVs-enabled wireless networks: Use cases, challenges, and open problems","volume":"8","author":"Brik","year":"2020","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MNET.011.1900317","article-title":"Federated learning for data privacy preservation in vehicular cyber-physical systems","volume":"34","author":"Lu","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4734","DOI":"10.1109\/TCOMM.2020.2990686","article-title":"Federated learning with blockchain for autonomous vehicles: Analysis and design challenges","volume":"68","author":"Pokhrel","year":"2020","journal-title":"IEEE Trans. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6798","DOI":"10.1109\/TVT.2020.2984369","article-title":"Improving TCP performance over WiFi for internet of vehicles: A federated learning approach","volume":"69","author":"Pokhrel","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hilmkil, A., Callh, S., Barbieri, M., S\u00fctfeld, L.R., Zec, E.L., and Mogren, O. (2021, January 23\u201325). Scaling federated learning for fine-tuning of large language models. Proceedings of the International Conference on Applications of Natural Language to Information Systems, Saarbr\u00fccken, Germany.","DOI":"10.1007\/978-3-030-80599-9_2"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lin, B.Y., He, C., Zeng, Z., Wang, H., Huang, Y., Dupuy, C., Gupta, R., Soltanolkotabi, M., Ren, X., and Avestimehr, S.A. (2021). FedNLP: Benchmarking federated learning methods for natural language processing tasks. arXiv.","DOI":"10.18653\/v1\/2022.findings-naacl.13"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ro, J.H., Breiner, T., McConnaughey, L., Chen, M., Suresh, A.T., Kumar, S., and Mathews, R. (2022). Scaling language model size in cross-device federated learning. arXiv.","DOI":"10.18653\/v1\/2022.fl4nlp-1.2"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s41666-020-00082-4","article-title":"Federated learning for healthcare informatics","volume":"5","author":"Xu","year":"2021","journal-title":"J. Healthc. Inform. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4641","DOI":"10.1109\/JIOT.2020.2964162","article-title":"Federated learning with cooperating devices: A consensus approach for massive IoT networks","volume":"7","author":"Savazzi","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1109\/TCOMM.2019.2956472","article-title":"Distributed federated learning for ultra-reliable low-latency vehicular communications","volume":"68","author":"Samarakoon","year":"2019","journal-title":"IEEE Trans. Commun."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.1109\/COMST.2021.3075439","article-title":"Federated learning for internet of things: A comprehensive survey","volume":"23","author":"Nguyen","year":"2021","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/IOTM.004.2100182","article-title":"Federated learning for the internet of things: Applications, challenges, and opportunities","volume":"5","author":"Zhang","year":"2022","journal-title":"IEEE Internet Things Mag."},{"key":"ref_21","unstructured":"Nguyen, J., Malik, K., Zhan, H., Yousefpour, A., Rabbat, M., Malek, M., and Huba, D. (2022, January 28\u201330). Federated learning with buffered asynchronous aggregation. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, J., Jia, J., Che, T., Huo, C., Ren, J., Zhou, Y., Dai, H., and Dou, D. (2024, January 20\u201327). Fedasmu: Efficient asynchronous federated learning with dynamic staleness-aware model update. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada.","DOI":"10.1609\/aaai.v38i12.29297"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Su, N., and Li, B. (2022, January 10\u201312). How asynchronous can federated learning be?. Proceedings of the 2022 IEEE\/ACM 30th International Symposium on Quality of Service (IWQoS), Virtual Event.","DOI":"10.1109\/IWQoS54832.2022.9812885"},{"key":"ref_24","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","volume":"33","author":"Lin","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1109\/TMC.2021.3070013","article-title":"Distillation-based semi-supervised federated learning for communication-efficient collaborative training with non-iid private data","volume":"22","author":"Itahara","year":"2021","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Cho, Y.J., Manoel, A., Joshi, G., Sim, R., and Dimitriadis, D. (2022). Heterogeneous ensemble knowledge transfer for training large models in federated learning. arXiv.","DOI":"10.24963\/ijcai.2022\/399"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cheng, G., Charles, Z., Garrett, Z., and Rush, K. (2022, January 19\u201324). Does federated dropout actually work?. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00382"},{"key":"ref_28","first-page":"12876","article-title":"Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout","volume":"34","author":"Horvath","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xu, Z., Yu, F., Xiong, J., and Chen, X. (2021, January 5\u20139). Helios: Heterogeneity-aware federated learning with dynamically balanced collaboration. Proceedings of the 58th ACM\/IEEE Design Automation Conference (DAC), San Francisco, CA, USA.","DOI":"10.1109\/DAC18074.2021.9586241"},{"key":"ref_30","first-page":"29677","article-title":"Fedrolex: Model-heterogeneous federated learning with rolling sub-model extraction","volume":"35","author":"Alam","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, Z., Lyu, L., Peng, Z., Yang, Z., Wen, C., Yu, R., Wang, C., and Fan, X. (2024, January 25\u201329). Fedsac: Dynamic submodel allocation for collaborative fairness in federated learning. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain.","DOI":"10.1145\/3637528.3671748"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3453476","article-title":"Federated learning for smart healthcare: A survey","volume":"55","author":"Nguyen","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1109\/TC.2020.2994391","article-title":"SAFA: A semi-asynchronous protocol for fast federated learning with low overhead","volume":"70","author":"Wu","year":"2020","journal-title":"IEEE Trans. Comput."},{"key":"ref_34","unstructured":"Phuong, M., and Lampert, C. (2019, January 9\u201315). Towards understanding knowledge distillation. Proceedings of the International Conference on Machine Learning (ICML), Long Beach, CA, USA."},{"key":"ref_35","first-page":"14068","article-title":"Group knowledge transfer: Federated learning of large CNNs at the edge","volume":"33","author":"He","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_36","first-page":"6906","article-title":"Does knowledge distillation really work?","volume":"34","author":"Stanton","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","first-page":"16070","article-title":"Attack of the tails: Yes, you really can backdoor federated learning","volume":"33","author":"Wang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_38","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_39","unstructured":"Diao, E., Ding, J., and Tarokh, V. (2020). HeteroFL: Computation and communication efficient federated learning for heterogeneous clients. arXiv."},{"key":"ref_40","unstructured":"Fu, Z., Yang, H., So, A.M.-C., Lam, W., Bing, L., and Collier, N. (2023, January 7\u201314). On the effectiveness of parameter-efficient fine-tuning. Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA."},{"key":"ref_41","unstructured":"Xiao, H., Rasul, K., and Vollgraf, R. (2017). Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms. arXiv."},{"key":"ref_42","unstructured":"Krizhevsky, A., and Hinton, G. (2009). Learning Multiple Layers of Features from Tiny Images, University of Toronto. Technical Report."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ilhan, F., Su, G., and Liu, L. (2023, January 18\u201323). ScaleFL: Resource-adaptive federated learning with heterogeneous clients. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02350"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/9\/226\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:36:04Z","timestamp":1760034964000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/9\/226"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,30]]},"references-count":44,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["bdcc9090226"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9090226","relation":{},"ISSN":["2504-2289"],"issn-type":[{"type":"electronic","value":"2504-2289"}],"subject":[],"published":{"date-parts":[[2025,8,30]]}}}