{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:59:14Z","timestamp":1755223154779,"version":"3.43.0"},"reference-count":38,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Trans Emerging Tel Tech"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>Blockchain acts as an important potential in the defense applications for several defense uses because of its features, namely transparency, decentralization, immutability, and security. However, protecting privacy data has various security liabilities and attack issues. Therefore, a new model named Residual Neuron Attention Network (ResNA\u2010Net) has been devised for privacy data protection in defense applications. In Federated Learning (FL), the entities, like server and nodes are included. Here, local training is done and the weights are updated to the server first, and next, model aggregation at the server is executed. Then, the global model is downloaded at all nodes, training is updated, and the process is iterated at all epochs. Meanwhile, in local training, the input defense data is normalized by Min\u2010max normalization and then augmented using oversampling. Then, k\u2010anonymization is executed using Fractional Gradient Beluga Whale Optimization (FGBWO). Next, privacy\u2010protected data classification is executed by employing ResNA\u2010Net, which is engineered by the combination of Deep Residual Network (DRN) and Neuron Attention Stage\u2010by\u2010Stage Net (NasNet). The ResNA\u2010Net achieved high performance and the immutable nature of the blockchain used in the ResNA\u2010Net model protects the defense data during the entire process of the system. The hybrid ResNA\u2010Net effectively learns the complex features and this capability improves the accuracy of the model. The high\u2010performance results obtained by the devised model highly protect sensitive data thereby providing security and privacy in defense data applications.<\/jats:p>","DOI":"10.1002\/ett.70182","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T22:07:01Z","timestamp":1749506821000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["K\u2010Anonymization and Residual Neuron Attention Network for Privacy Data Protection in Blockchain Network With Federated Learning Using Defense Application"],"prefix":"10.1002","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1818-9689","authenticated-orcid":false,"given":"T.","family":"Premkumar","sequence":"first","affiliation":[{"name":"Department of Computer Applications PG, Vels Institute of Science Technology and Advanced Studies  Chennai India"}]},{"given":"D. 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