{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T19:45:54Z","timestamp":1768679154356,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T00:00:00Z","timestamp":1705276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea government (MSIT)","award":["RS-2022-00166739"],"award-info":[{"award-number":["RS-2022-00166739"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Machine learning techniques have attracted considerable attention for wireless networks because of their impressive performance in complicated scenarios and usefulness in various applications. However, training with and sharing raw data obtained locally from each wireless node does not guarantee privacy and requires a large communication overhead. To mitigate such issues, federated learning (FL), in which sharing parameters for model updates are shared instead of raw data, has been developed. FL has also been studied using blockchain techniques to efficiently perform learning in distributed wireless systems without having to deploy a centralized server. Although blockchain-based decentralized federated learning (BDFL) is a promising technique for various wireless sensor networks, malicious attacks can still occur, which result in performance degradation or malfunction. In this study, we analyze the impact of a jamming threats from malicious miners to BDFL in wireless networks. In a wireless BDFL system, it is possible for malicious miners with jamming capability to interfere with the collection of model parameters by normal miners, thus preventing the victim miner from generating a global model. By disrupting normal miners participating in BDFL systems, malicious miners with jamming capability can more easily add malicious data to the mainstream. Through various simulations, we evaluated the success probability performance of malicious block insertion and the participation rate of normal miners in a wireless BDFL system.<\/jats:p>","DOI":"10.3390\/s24020535","type":"journal-article","created":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T08:52:03Z","timestamp":1705308723000},"page":"535","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["The Threat of Disruptive Jamming to Blockchain-Based Decentralized Federated Learning in Wireless Networks"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3782-2609","authenticated-orcid":false,"given":"Gyungmin","family":"Kim","sequence":"first","affiliation":[{"name":"Agency for Defense Development, Daejeon 34186, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3622-1337","authenticated-orcid":false,"given":"Yonggang","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Computer Science and Engineering, Kongju National University, Cheonan 31080, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3072","DOI":"10.1109\/COMST.2019.2924243","article-title":"Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues","volume":"21","author":"Sun","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MCE.2019.2959108","article-title":"Preserving data privacy via federated learning: Challenges and solutions","volume":"9","author":"Li","year":"2020","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MCOM.001.1900461","article-title":"Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges","volume":"58","author":"Niknam","year":"2020","journal-title":"IEEE Commun. Mag."},{"key":"ref_4","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B.A. (2017, January 20\u201322). Communication-efficient learning of deep networks from decentralized data. Proceedings of the Artificial Intelligence and Statistics, Lauderdale, FL, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4220","DOI":"10.1109\/TNSE.2022.3196463","article-title":"Federated learning in massive mimo 6 g networks: Convergence analysis and communication-efficient design","volume":"9","author":"Mu","year":"2022","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1016\/j.comcom.2020.01.023","article-title":"Blockchain envisioned UAV networks: Challenges, solutions, and comparisons","volume":"151","author":"Mehta","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ye, C., Li, G., Cai, H., Gu, Y., and Fukuda, A. (2018, January 22\u201323). Analysis of security in blockchain: Case study in 51%-attack detecting. Proceedings of the 2018 5th International Conference on Dependable Systems and Their Applications (DSA), Dalian, China.","DOI":"10.1109\/DSA.2018.00015"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"D126","DOI":"10.1364\/JOCN.10.00D126","article-title":"Machine learning for network automation: Overview, architecture, and applications [Invited Tutorial]","volume":"10","author":"Rafique","year":"2018","journal-title":"J. Opt. Commun. Netw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4363","DOI":"10.1109\/TNSE.2022.3200057","article-title":"Optimized Multi-Service Tasks Offloading for Federated Learning in Edge Virtualization","volume":"9","author":"Tam","year":"2022","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4177","DOI":"10.1109\/TII.2019.2942190","article-title":"Blockchain and federated learning for privacy-preserved data sharing in industrial IoT","volume":"16","author":"Lu","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"12806","DOI":"10.1109\/JIOT.2021.3072611","article-title":"Federated learning meets blockchain in edge computing: Opportunities and challenges","volume":"8","author":"Nguyen","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/JSAC.2022.3213345","article-title":"A Fast blockchain-based federated learning framework with compressed communications","volume":"40","author":"Cui","year":"2022","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103320","DOI":"10.1016\/j.adhoc.2023.103320","article-title":"Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: A survey","volume":"152","author":"Ali","year":"2024","journal-title":"Ad Hoc Netw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2983","DOI":"10.1109\/COMST.2023.3315746","article-title":"Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges","volume":"25","author":"Bernal","year":"2023","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"140549","DOI":"10.1109\/ACCESS.2021.3119291","article-title":"The 51% attack on blockchains: A mining behavior study","volume":"9","author":"Orozco","year":"2021","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"95033","DOI":"10.1109\/ACCESS.2019.2928753","article-title":"Regional blockchain for vehicular networks to prevent 51% attacks","volume":"7","author":"Shrestha","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yang, X., Chen, Y., and Chen, X. (2019, January 14\u201317). Effective scheme against 51% attack on proof-of-work blockchain with history weighted information. Proceedings of the 2019 IEEE International Conference on Blockchain (Blockchain), Atlanta, GA, USA.","DOI":"10.1109\/Blockchain.2019.00041"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2529","DOI":"10.1109\/COMST.2023.3323640","article-title":"A survey on X. 509 public-key infrastructure, certificate revocation, and their modern implementation on blockchain and ledger technologies","volume":"25","author":"Khan","year":"2023","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2604","DOI":"10.3390\/app13042604","article-title":"A Survey on Consensus Protocols and Attacks on Blockchain Technology","volume":"13","author":"Guru","year":"2023","journal-title":"Appl. Sci."},{"key":"ref_21","unstructured":"Gangwani, P., Bhardwaj, T., Perez-Pons, A., Upadhyay, H., and Lagos, L. (2023). Artificial Intelligence in Cyber-Physical Systems, CRC Press."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/JBHI.2022.3183644","article-title":"Anti-jamming strategy for federated learning in internet of medical things: A game approach","volume":"27","author":"Ruby","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1109\/LWC.2020.2971469","article-title":"RAFT based wireless blockchain networks in the presence of malicious jamming","volume":"9","author":"Xu","year":"2020","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1109\/TPDS.2020.3044223","article-title":"Biscotti: A blockchain system for private and secure federated learning","volume":"32","author":"Shayan","year":"2020","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Shi, Y., and Sagduyu, Y.E. (2022, January 4\u20138). How to Launch Jamming Attacks on Federated Learning in NextG Wireless Networks. Proceedings of the 2022 IEEE Globecom Workshops (GC Wkshps), Rio de Janeiro, Brazil.","DOI":"10.1109\/GCWkshps56602.2022.10008669"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kim, G., Kim, Y., Park, J., and Lim, H. (2018, January 11\u201313). Frame-selective wireless attack using deep-learning-based length prediction. Proceedings of the IEEE International Conference on Sensing, Communication, and Networking (SECON), Hong Kong, China.","DOI":"10.1109\/SAHCN.2018.8397145"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2792","DOI":"10.1109\/TWC.2015.2510643","article-title":"Jamming bandits\u2014A novel learning method for optimal jamming","volume":"15","author":"Amuru","year":"2015","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"210127","DOI":"10.1109\/ACCESS.2020.3039568","article-title":"Reinforcement learning based beamforming jammer for unknown wireless networks","volume":"8","author":"Kim","year":"2020","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1109\/COMST.2022.3159185","article-title":"Jamming attacks and anti-jamming strategies in wireless networks: A comprehensive survey","volume":"24","author":"Pirayesh","year":"2022","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"14691","DOI":"10.1109\/JSEN.2023.3274687","article-title":"Countering active attacks on RAFT-based IoT blockchain networks","volume":"23","author":"Buttar","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lee, J., Kim, G., and Kim, Y. (2022, January 19\u201321). Crisis analysis on blockchain-based decentralized learning in wireless networks. Proceedings of the International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea.","DOI":"10.1109\/ICTC55196.2022.9952554"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3628","DOI":"10.1109\/TVT.2010.2050503","article-title":"Optimal duplex mode for DF relay in terms of the outage probability","volume":"59","author":"Kwon","year":"2010","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_33","unstructured":"Rosenfeld, M. (2014). Analysis of hashrate-based double spending. arXiv."},{"key":"ref_34","first-page":"344","article-title":"Random walk and ruin problems","volume":"1","author":"Feller","year":"1968","journal-title":"Introd. Probab. Theory Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4530","DOI":"10.1109\/TMC.2022.3162117","article-title":"BLOWN: A blockchain protocol for single-hop wireless networks under adversarial SINR","volume":"22","author":"Xu","year":"2022","journal-title":"IEEE Trans. Mob. Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/535\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:47:14Z","timestamp":1760104034000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/535"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,15]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24020535"],"URL":"https:\/\/doi.org\/10.3390\/s24020535","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,15]]}}}