{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:41:02Z","timestamp":1777704062765,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EXIGENCE","award":["101139120"],"award-info":[{"award-number":["101139120"]}]},{"name":"EXIGENCE","award":["UIDB\/50008"],"award-info":[{"award-number":["UIDB\/50008"]}]},{"name":"EXIGENCE","award":["C645112083-00000059"],"award-info":[{"award-number":["C645112083-00000059"]}]},{"DOI":"10.13039\/501100001871","name":"FCT - Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia, I.P.","doi-asserted-by":"publisher","award":["101139120"],"award-info":[{"award-number":["101139120"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"FCT - Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia, I.P.","doi-asserted-by":"publisher","award":["UIDB\/50008"],"award-info":[{"award-number":["UIDB\/50008"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"FCT - Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia, I.P.","doi-asserted-by":"publisher","award":["C645112083-00000059"],"award-info":[{"award-number":["C645112083-00000059"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"PRR \u2013 Plano de Recupera\u00e7\u00e3o e Resili\u00eancia","award":["101139120"],"award-info":[{"award-number":["101139120"]}]},{"name":"PRR \u2013 Plano de Recupera\u00e7\u00e3o e Resili\u00eancia","award":["UIDB\/50008"],"award-info":[{"award-number":["UIDB\/50008"]}]},{"name":"PRR \u2013 Plano de Recupera\u00e7\u00e3o e Resili\u00eancia","award":["C645112083-00000059"],"award-info":[{"award-number":["C645112083-00000059"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The increasing demand for distributed machine learning like Federated Learning (FL) in dynamic, resource-constrained edge environments, 5G\/6G networks, and the proliferation of mobile and edge devices, presents significant challenges related to fault tolerance, elasticity, and communication efficiency. This paper addresses these issues by proposing a novel modular and resilient FL framework. In this context, resilience refers to the system\u2019s ability to maintain operation and performance despite disruptions. The framework is built on decoupled modules handling core FL functionalities, allowing flexibility in integrating various algorithms, communication protocols, and resilience strategies. Results demonstrate the framework\u2019s ability to integrate different communication protocols and FL paradigms, showing that protocol choice significantly impacts performance, particularly in high-volume communication scenarios, with Zenoh and MQTT exhibiting lower overhead than Kafka in tested configurations, and Zenoh emerging as the most efficient communication option. Additionally, the framework successfully maintained model training and achieved convergence even when simulating probabilistic worker failures, achieving a MCC of 0.9453.<\/jats:p>","DOI":"10.3390\/s25123812","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T09:36:00Z","timestamp":1750239360000},"page":"3812","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Federated Learning for a Dynamic Edge: A Modular and Resilient Approach"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5669-2378","authenticated-orcid":false,"given":"Leonardo","family":"Almeida","sequence":"first","affiliation":[{"name":"Intituto de Telecomunica\u00e7\u00f5es, 3810-193 Aveiro, Portugal"},{"name":"Departamento de Eletr\u00f3nica, Telecomunica\u00e7\u00f5es e Inform\u00e1tica, University of Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7211-382X","authenticated-orcid":false,"given":"Rafael","family":"Teixeira","sequence":"additional","affiliation":[{"name":"Intituto de Telecomunica\u00e7\u00f5es, 3810-193 Aveiro, Portugal"},{"name":"Departamento de Eletr\u00f3nica, Telecomunica\u00e7\u00f5es e Inform\u00e1tica, University of Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9712-0639","authenticated-orcid":false,"given":"Gabriele","family":"Baldoni","sequence":"additional","affiliation":[{"name":"Gabriele Baldoni Consulting, 75000 Paris, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6504-9441","authenticated-orcid":false,"given":"M\u00e1rio","family":"Antunes","sequence":"additional","affiliation":[{"name":"Intituto de Telecomunica\u00e7\u00f5es, 3810-193 Aveiro, Portugal"},{"name":"Departamento de Eletr\u00f3nica, Telecomunica\u00e7\u00f5es e Inform\u00e1tica, University of Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0107-6253","authenticated-orcid":false,"given":"Rui L.","family":"Aguiar","sequence":"additional","affiliation":[{"name":"Intituto de Telecomunica\u00e7\u00f5es, 3810-193 Aveiro, Portugal"},{"name":"Departamento de Eletr\u00f3nica, Telecomunica\u00e7\u00f5es e Inform\u00e1tica, University of Aveiro, 3810-193 Aveiro, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dritsas, E., and Trigka, M. (2025). Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications. J. Sens. Actuator Netw., 14.","DOI":"10.3390\/jsan14010009"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MNET.006.2200437","article-title":"Optimization design for federated learning in heterogeneous 6g networks","volume":"37","author":"Luo","year":"2023","journal-title":"IEEE Netw."},{"key":"ref_3","unstructured":"Xu, R., Baracaldo, N., and Joshi, J. (2021). Privacy-preserving machine learning: Methods, challenges and directions. arXiv."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","unstructured":"Durovic, M., and Corno, T. (2024). The privacy of emotions: From the GDPR to the AI Act, an overview of emotional AI regulation and the protection of privacy and personal data. Privacy, Data Protection and Data-Driven Technologies, Routledge.","DOI":"10.4324\/9781003502791-18"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"14071","DOI":"10.1109\/JIOT.2023.3250275","article-title":"A comprehensive empirical study of heterogeneity in federated learning","volume":"10","author":"Abdelmoniem","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, B., Ivanov, N., Wang, G., and Yan, Q. (2023, January 11\u201314). DynamicFL: Balancing communication dynamics and client manipulation for federated learning. Proceedings of the 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Madrid, Spain.","DOI":"10.1109\/SECON58729.2023.10287430"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Teixeira, R., Antunes, M., Gomes, D., and Aguiar, R.L. (2023, January 11\u201314). The learning costs of Federated Learning in constrained scenarios. Proceedings of the 2023 10th International Conference on Future Internet of Things and Cloud (FiCloud), Marrakesh, Morocco.","DOI":"10.1109\/FiCloud58648.2023.00011"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Almeida, L., Rodrigues, P., Teixeira, R., Antunes, M., and Aguiar, R.L. (2024, January 25\u201327). Privacy-Preserving Defense: Intrusion Detection in IoT using Federated Learning. Proceedings of the 2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON), Porto, Portugal.","DOI":"10.1109\/MELECON56669.2024.10608461"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2802","DOI":"10.1109\/TPDS.2020.3003307","article-title":"Distributed Training of Deep Learning Models: A Taxonomic Perspective","volume":"31","author":"Langer","year":"2020","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1007\/s10115-022-01664-x","article-title":"From distributed machine learning to federated learning: A survey","volume":"64","author":"Liu","year":"2022","journal-title":"Knowl. Inf. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3339474","article-title":"Federated machine learning: Concept and applications","volume":"10","author":"Yang","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., and Yu, H. (2020). Horizontal federated learning. Federated Learning, Springer.","DOI":"10.1007\/978-3-031-01585-4"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3615","DOI":"10.1109\/TKDE.2024.3352628","article-title":"Vertical federated learning: Concepts, advances, and challenges","volume":"36","author":"Liu","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_15","first-page":"35","article-title":"Federated transfer learning: Concept and applications","volume":"15","author":"Saha","year":"2021","journal-title":"Intell. Artif."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, L., Fan, Y., Tse, M., and Lin, K.Y. (2020). A review of applications in federated learning. Comput. Ind. Eng., 149.","DOI":"10.1016\/j.cie.2020.106854"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gabriel, E., Fagg, G.E., Bosilca, G., Angskun, T., Dongarra, J.J., Squyres, J.M., Sahay, V., Kambadur, P., Barrett, B., and Lumsdaine, A. (2004, January 19\u201322). Open MPI: Goals, concept, and design of a next generation MPI implementation. Proceedings of the Recent Advances in Parallel Virtual Machine and Message Passing Interface: 11th European PVM\/MPI Users\u2019 Group Meeting, Budapest, Hungary. Proceedings 11.","DOI":"10.1007\/978-3-540-30218-6_19"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Light, R.A. (2017). Mosquitto: Server and client implementation of the MQTT protocol. J. Open Source Softw., 2.","DOI":"10.21105\/joss.00265"},{"key":"ref_19","unstructured":"Kreps, J., Narkhede, N., and Rao, J. (2011, January 12\u201316). Kafka: A distributed messaging system for log processing. Proceedings of the NetDB, Athens, Greece."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Corsaro, A., Cominardi, L., Hecart, O., Baldoni, G., Avital, J.E.P., Loudet, J., Guimares, C., Ilyin, M., and Bannov, D. (2023, January 6\u20138). Zenoh: Unifying communication, storage and computation from the cloud to the microcontroller. Proceedings of the 2023 26th Euromicro Conference on Digital System Design (DSD), Golem, Albania.","DOI":"10.1109\/DSD60849.2023.00065"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jayaram, K.R., Muthusamy, V., Thomas, G., Verma, A., and Purcell, M. (2022, January 17\u201320). Adaptive Aggregation For Federated Learning. Proceedings of the 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan.","DOI":"10.1109\/BigData55660.2022.10021119"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TNNLS.2024.3362974","article-title":"Multicenter Hierarchical Federated Learning With Fault-Tolerance Mechanisms for Resilient Edge Computing Networks","volume":"36","author":"Chen","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dautov, R., and Husom, E.J. (2024, January 15\u201316). Raft Protocol for Fault Tolerance and Self-Recovery in Federated Learning. Proceedings of the 19th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, Lisbon, Portugal.","DOI":"10.1145\/3643915.3644093"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"120918","DOI":"10.1109\/ACCESS.2023.3328310","article-title":"Privacy-Preserving Big Data Security for IoT with Federated Learning and Cryptography","volume":"11","author":"Awan","year":"2023","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bano, S. (2021, January 23\u201327). PhD Forum Abstract: Efficient Computing and Communication Paradigms for Federated Learning Data Streams. Proceedings of the 2021 IEEE International Conference on Smart Computing (SMARTCOMP), Irvine, CA, USA.","DOI":"10.1109\/SMARTCOMP52413.2021.00086"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3047","DOI":"10.1109\/TNSE.2022.3153519","article-title":"EPPDA: An Efficient Privacy-Preserving Data Aggregation Federated Learning Scheme","volume":"10","author":"Song","year":"2023","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mansouri, M., \u00d6nen, M., and Ben Jaballah, W. (2022, January 5\u20139). Learning from Failures: Secure and Fault-Tolerant Aggregation for Federated Learning. Proceedings of the 38th Annual Computer Security Applications Conference, Austin, TX, USA.","DOI":"10.1145\/3564625.3568135"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.future.2022.02.024","article-title":"Dynamic and adaptive fault-tolerant asynchronous federated learning using volunteer edge devices","volume":"133","author":"Morell","year":"2022","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cleland, G., Wu, D., Ullah, R., and Varghese, B. (2022, January 6\u20139). FedComm: Understanding communication protocols for edge-based federated learning. Proceedings of the 2022 IEEE\/ACM 15th International Conference on Utility and Cloud Computing (UCC), Vancouver, WA, USA.","DOI":"10.1109\/UCC56403.2022.00018"},{"key":"ref_30","unstructured":"Parsa, I. (1998). KDD Cup 1998 Data, UCI Machine Learning Repository."},{"key":"ref_31","unstructured":"Stolfo, S., Fan, W., Lee, W., Prodromidis, A., and Chan, P. (1999). KDD Cup 1999 Data, UCI Machine Learning Repository."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A.A. (2009, January 8\u201310). A detailed analysis of the KDD CUP 99 data set. Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, ON, Canada.","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"ref_33","unstructured":"Kostas, K., Just, M., and Lones, M.A. (2023). IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Moustafa, N., and Slay, J. (2015, January 10\u201312). UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). Proceedings of the 2015 Military Communications and Information Systems Conference (MilCIS), Canberra, ACT, Australia.","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s11036-021-01843-0","article-title":"Towards a Standard Feature Set for Network Intrusion Detection System Datasets","volume":"27","author":"Sarhan","year":"2021","journal-title":"Mob. Netw. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Abou Khamis, R., and Matrawy, A. (2020, January 20\u201322). Evaluation of adversarial training on different types of neural networks in deep learning-based idss. Proceedings of the 2020 International Symposium on Networks, Computers and Communications (ISNCC), Montreal, QC, Canada.","DOI":"10.1109\/ISNCC49221.2020.9297344"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Pi\u00f1eiro, C., and Pichel, J.C. (2024). OMP4Py: A pure Python implementation of OpenMP. arXiv.","DOI":"10.2139\/ssrn.5041369"},{"key":"ref_38","unstructured":"Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., and Chandra, V. (2018). Federated learning with non-iid data. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Guendouzi, B.S., Ouchani, S., Assaad, H.E., and Zaher, M.E. (2023). A systematic review of federated learning: Challenges, aggregation methods, and development tools. J. Netw. Comput. Appl., 220.","DOI":"10.1016\/j.jnca.2023.103714"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1007\/s11277-020-07134-3","article-title":"Lightweight cryptography: A solution to secure IoT","volume":"112","author":"Dhanda","year":"2020","journal-title":"Wirel. Pers. Commun."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1007\/s10796-023-10383-9","article-title":"Analysis of the Cryptographic Algorithms in IoT Communications","volume":"26","author":"Silva","year":"2023","journal-title":"Inf. Syst. Front."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/12\/3812\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:54:21Z","timestamp":1760032461000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/12\/3812"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,18]]},"references-count":41,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["s25123812"],"URL":"https:\/\/doi.org\/10.3390\/s25123812","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,18]]}}}