[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123389-en":3,"doc-seo-123389-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123389,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","A blockchain solution for decentralized training in machine learning for IoT - Secure and efficient federated learning","The rapid growth of Internet of Things (IoT) devices increases the need for machine learning methods that can handle privacy, security, and scalability constraints. Federated learning (FL) and blockchain are leveraged to enable decentralized, privacy-preserving model training across distributed data sources. This paper presents an IoT architecture combining the incremental learning vector quantization algorithm (XuILVQ) with Ethereum to support secure data sharing, model training, and prototype storage. Experiments evaluate performance and show gains in accuracy and efficiency while reducing communication and computational overhead.","Computer Communications 242 (2025) 108289  \n| A blockchain solution for decentralized training in machine learning for IoT Carlos Beis-Penedo a ,∗, Francisco Troncoso-Pastoriza a,b, Rebeca P. Díaz-Redondo a, Ana Fernández-Vilasa, Manuel Fernández-Veiga a, Martín González Sotoa,c\u003Cbr>a atlanTTic, I&C Lab, Escuela de Ingeniería de Telecomunicación. Campus universitario s/n, Vigo, 36310, Spain b Centro Universitario de la Defensa en la Escuela Naval Militar, Plaza de España, s/n, Marín, 36920, Spain c Centro Tecnolóxico de Telecomunicacións de Galicia (GRADIANT), Carretera do Vilar, 56-58, Vigo, 36214, Spain |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Internet of Things (IoT) Federated learning Blockchain\u003Cbr>Incremental learning vector quantization (XuILVQ)\u003Cbr>Secure aggregation |  | The rapid growth of Internet of Things (IoT) devices and applications has led to an increased demand for advanced analytics and machine learning techniques capable of handling the challenges associated with data privacy, security, and scalability. Federated learning (FL) and blockchain technologies have emerged as promising approaches to address these challenges by enabling decentralized, secure, and privacy-preserving model training on distributed data sources. In this paper, we present a novel IoT solution that combines the incremental learning vector quantization algorithm (XuILVQ) with Ethereum blockchain technology to facilitate secure and efficient data sharing, model training, and prototype storage in a distributed environment. Our proposed architecture addresses the shortcomings of existing blockchain-based FL solutions by reducing computational and communication overheads while maintaining data privacy and security. We assess the performance of our system through a series of experiments, showing its potential to enhance the accuracy and efficiency of machine learning tasks in IoT settings. |  |\n\n1. Introduction  \nThe proliferation of Internet of Things (IoT) devices and applications has generated massive amounts of data that require advanced analytics and machine learning techniques for meaningful insights [1]. IoT is impacting a wide range of industries, including smart cities, healthcare, industrial automation or transportation, by enabling realtime monitoring and control capabilities. However, traditional centralized machine learning models face challenges such as data privacy, security, and scalability. Federated learning (FL) [2] is an emerging technique that addresses these challenges by enabling decentralized model training on distributed data sources while preserving data privacy and security. Despite its promise, FL still faces several technical challenges such as non-iid data distribution, communication overhead, and straggler nodes [3].  \nIn the traditional FL approach, multiple devices work together to train a machine learning model. Each node keeps its own data set that is not shared, thus, data resides on trusted nodes. This scenario is particularly convenient for IoT applications, where devices often generate sensitive data that must be protected from unauthorized access. Each node locally trains its own model, which is later shared with the other nodes within the FL setting. These local models are  \naggregated to build up a global model without exposing the local data. However, this exchange of model updates introduces new security and privacy concerns [4]. Some of the most widely known security challenges are related to protect the FL setting against the following attacks: (i) data poisoning attacks [5], where malicious nodes inject corrupted or misleading data into the training process, compromising the accuracy of the global model; (ii) model inversion attacks [6], where adversaries aim to reconstruct individual data samples from aggregated model updates, potentially revealing sensitive information;  \n(iii) sibyl attacks [7,8], where malicious entities create multipl","cbCairnf7hPzqtZB","https://ap.wps.com/l/cbCairnf7hPzqtZB","pdf",1868357,1,12,"English","en",105,"# Introduction\n## Challenges in IoT machine learning\n## Federated learning fundamentals\n## Security and privacy threats in FL\n## Blockchain-based FL for secure collaboration","[{\"question\":\"Why are federated learning and blockchain combined for IoT training?\",\"answer\":\"Federated learning enables decentralized model training without sharing raw local data, while blockchain provides a tamper-proof ledger for secure data, model, and training-result storage and sharing, improving privacy and security.\"},{\"question\":\"What are the main security threats discussed for federated learning?\",\"answer\":\"The document highlights data poisoning attacks, model inversion attacks, Sybil attacks with fake nodes, and collusion attacks where multiple malicious nodes jointly manipulate the global model.\"},{\"question\":\"How does the proposed solution improve efficiency compared with existing blockchain-based FL approaches?\",\"answer\":\"The architecture reduces computational and communication overheads while maintaining data privacy and security, and performance is validated through experiments on IoT machine learning tasks.\"}]","A blockchain solution for decentralized training in machine learning for IoT - Secure and efficient federated learning | PDF",1785816244,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-blockchain-solution-for-decentralized-training-in-machine-learning-for-iot-secure-and-efficient-federated-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-blockchain-solution-for-decentralized-training-in-machine-learning-for-iot-secure-and-efficient-federated-learning/123389/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are federated learning and blockchain combined for IoT training?","Question",{"text":75,"@type":76},"Federated learning enables decentralized model training without sharing raw local data, while blockchain provides a tamper-proof ledger for secure data, model, and training-result storage and sharing, improving privacy and security.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the main security threats discussed for federated learning?",{"text":80,"@type":76},"The document highlights data poisoning attacks, model inversion attacks, Sybil attacks with fake nodes, and collusion attacks where multiple malicious nodes jointly manipulate the global model.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed solution improve efficiency compared with existing blockchain-based FL approaches?",{"text":84,"@type":76},"The architecture reduces computational and communication overheads while maintaining data privacy and security, and performance is validated through experiments on IoT machine learning tasks.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]