{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T02:03:43Z","timestamp":1768442623122,"version":"3.49.0"},"reference-count":24,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,12,18]],"date-time":"2020-12-18T00:00:00Z","timestamp":1608249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>During the last decade, the Internet of Things acted as catalyst for the big data phenomenon. As result, modern edge devices can access a huge amount of data that can be exploited to build useful services. In such a context, artificial intelligence has a key role to develop intelligent systems (e.g., intelligent cyber physical systems) that create a connecting bridge with the physical world. However, as time goes by, machine and deep learning applications are becoming more complex, requiring increasing amounts of data and training time, which makes the use of centralized approaches unsuitable. Federated learning is an emerging paradigm which enables the cooperation of edge devices to learn a shared model (while keeping private their training data), thereby abating the training time. Although federated learning is a promising technique, its implementation is difficult and brings a lot of challenges. In this paper, we present an extension of Stack4Things, a cloud platform developed in our department; leveraging its functionalities, we enabled the deployment of federated learning on edge devices without caring their heterogeneity. Experimental results show a comparison with a centralized approach and demonstrate the effectiveness of the proposed approach in terms of both training time and model accuracy.<\/jats:p>","DOI":"10.3390\/jsan9040059","type":"journal-article","created":{"date-parts":[[2020,12,18]],"date-time":"2020-12-18T11:42:09Z","timestamp":1608291729000},"page":"59","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Leveraging Stack4Things for Federated Learning in Intelligent Cyber Physical Systems"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6709-8001","authenticated-orcid":false,"given":"Fabrizio","family":"De Vita","sequence":"first","affiliation":[{"name":"Department of Engineering, University of Messina, 98166 Messina, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6080-9077","authenticated-orcid":false,"given":"Dario","family":"Bruneo","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Messina, 98166 Messina, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lee, E.A. (2008, January 5\u20137). Cyber Physical Systems: Design Challenges. Proceedings of the 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC), Orlando, FL, USA.","DOI":"10.1109\/ISORC.2008.25"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Imteaj, A., and Amini, M.H. (2019, January 5\u20137). Distributed Sensing Using Smart End-User Devices: Pathway to Federated Learning for Autonomous IoT. Proceedings of the 2019 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.","DOI":"10.1109\/CSCI49370.2019.00218"},{"key":"ref_3","unstructured":"Li, Q., Wen, Z., and He, B. (2019). Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yang, Q., Liu, Y., Chen, T., and Tong, Y. (2019). Federated Machine Learning: Concept and Applications. ACM Trans. Intell. Syst. Technol., 10.","DOI":"10.1145\/3298981"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7751","DOI":"10.1109\/JIOT.2020.2991401","article-title":"Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach","volume":"7","author":"Liu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yao, X., Huang, T., Wu, C., Zhang, R., and Sun, L. (2019, January 22\u201325). Towards Faster and Better Federated Learning: A Feature Fusion Approach. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803001"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Koz\u0142owski, E., Mazurkiewicz, D., \u017babi\u0144ski, T., Prucnal, S., and S\u0119p, J. (2020). Machining sensor data management for operation-level predictive model. Expert Syst. Appl., 159.","DOI":"10.1016\/j.eswa.2020.113600"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.eswa.2017.05.079","article-title":"Fault detection and explanation through big data analysis on sensor streams","volume":"87","author":"Manco","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s12243-016-0528-5","article-title":"Stack4Things: A Sensing-and-Actuation-as-a-Service Framework for IoT and Cloud Integration","volume":"72","author":"Longo","year":"2017","journal-title":"Ann. Telecommun."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, Y., Nie, J., Li, X., Ahmed, S.H., Lim, W.Y.B., and Miao, C. (2020). Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2020.3021006"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Zhu, X., Wang, J., Hong, Z., Xia, T., and Xiao, J. (2019, January 4\u20136). Federated Learning of Unsegmented Chinese Text Recognition Model. Proceedings of the 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), Portland, OR, USA.","DOI":"10.1109\/ICTAI.2019.00186"},{"key":"ref_13","unstructured":"Beutel, D.J., Topal, T., Mathur, A., Qiu, X., Parcollet, T., and Lane, N.D. (2020). Flower: A Friendly Federated Learning Research Framework. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/MNET.2019.1800286","article-title":"In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning","volume":"33","author":"Wang","year":"2019","journal-title":"IEEE Netw."},{"key":"ref_15","unstructured":"Caldas, S., Duddu, S.M.K., Wu, P., Li, T., Kone\u010dn\u00fd, J., McMahan, H.B., Smith, V., and Talwalkar, A. (2019). LEAF: A Benchmark for Federated Settings. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yang, H., He, H., Zhang, W., and Cao, X. (2020). FedSteg: A Federated Transfer Learning Framework for Secure Image Steganalysis. IEEE Trans. Netw. Sci. Eng.","DOI":"10.1109\/TNSE.2020.2996612"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/OJCS.2020.2993259","article-title":"Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge Based Framework","volume":"1","author":"Wu","year":"2020","journal-title":"IEEE Open J. Comput. Soc."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"De Vita, F., Bruneo, D., and Das, S.K. (2020, January 21\u201324). A Novel Data Collection Framework for Telemetry and Anomaly Detection in Industrial IoT Systems. Proceedings of the 2020 IEEE\/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI), Sydney, Australia.","DOI":"10.1109\/IoTDI49375.2020.00032"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3400","DOI":"10.1109\/TNNLS.2019.2944481","article-title":"Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data","volume":"31","author":"Sattler","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_20","unstructured":"Kone\u010dn\u00fd, J., McMahan, H.B., Yu, F.X., Richtarik, P., Suresh, A.T., and Bacon, D. (2016, January 9). Federated Learning: Strategies for Improving Communication Efficiency. Proceedings of the NIPS Workshop on Private Multi-Party Machine Learning, Barcelona, Spain."},{"key":"ref_21","unstructured":"Bonawitz, K.A., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C.M., Kone\u010dn\u00fd, J., Mazzocchi, S., and McMahan, B. (2019). Towards Federated Learning at Scale: System Design. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5129","DOI":"10.1109\/TIP.2018.2848705","article-title":"MIO-TCD: A New Benchmark Dataset for Vehicle Classification and Localization","volume":"27","author":"Luo","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Nishio, T., and Yonetani, R. (2019, January 20\u201324). Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge. Proceedings of the ICC 2019\u20142019 IEEE International Conference on Communications (ICC), Shanghai, China.","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref_24","first-page":"2021","article-title":"FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization","volume":"108","author":"Reisizadeh","year":"2020","journal-title":"Proc. Mach. Learn. Res."}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/9\/4\/59\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:46:49Z","timestamp":1760179609000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/9\/4\/59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,18]]},"references-count":24,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["jsan9040059"],"URL":"https:\/\/doi.org\/10.3390\/jsan9040059","relation":{},"ISSN":["2224-2708"],"issn-type":[{"value":"2224-2708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,18]]}}}