{ "ok": true, "doi": "10.46243/jst.2025.v10.i07.pp29-36", "events": [ { "seq": 1, "kind": "register", "at": "2026-09-29 22:00:32", "by": "Administrator (admin)", "by_kind": "user", "note": "registered at Crossref; record read from api.crossref.org", "changes": [ { "path": "abstract.lang", "from": null, "to": "en" }, { "path": "abstract.value", "from": null, "to": "Today's digital age has made privacy and data protection a major concern-generally, with the kind of technologies that are turning things around and bringing everything to the cloud. FL will most likely provide a solution to the distance and make things clear in collaboration without exposing raw information from a consortium to boost its privacy. However, existing FL solutions include such challenges as increased overhead communication, risk in leaking data, and even the inefficiency of secure aggregation.To mitigate these constraints, this research proposes the Autoencoder-Based Federated Learning framework by integrating prevailing techniques such as differential privacy and homomorphic encryption that safeguard both the security and efficiency of the model. This method does not only steal model ideas for autoencoders to compress before sciences transmission but hugely reduces the transmission bandwidth and possibly minimizes gradient leakage. However, adaptive normalization is used to handle institutional heterogeneity to maintain better performance for the model. Conclusion of experimentation indicated that this framework could significantly reduce communication overhead while retaining high federated learning accuracy and even better security. Further, the trust-based client evaluation mechanism is presented to detect malicious behavior and improve reliability regarding federated aggregation. The experiment showed that Autoencoder Based Federated Learning was a scalable, secure, and privacy-efficient solution to applications tailored for healthcare, finance, and other sensitive data environments." }, { "path": "agents.0.name.family", "from": null, "to": "Singh Chauhan" }, { "path": "agents.0.name.given", "from": null, "to": "Guman" }, { "path": "agents.0.role", "from": null, "to": "author" }, { "path": "agents.0.sequence", "from": null, "to": "first" }, { "path": "agents.1.name.family", "from": null, "to": "Teja Gollapalli" }, { "path": "agents.1.name.given", "from": null, "to": "Venkata Surya" }, { "path": "agents.1.role", "from": null, "to": "author" }, { "path": "agents.1.sequence", "from": null, "to": "additional" }, { "path": "agents.2.name.family", "from": null, "to": "Srinivasan" }, { "path": "agents.2.name.given", "from": null, "to": "Kannan" }, { "path": "agents.2.role", "from": null, "to": "author" }, { "path": "agents.2.sequence", "from": null, "to": "additional" }, { "path": "agents.3.name.family", "from": null, "to": "Jadon" }, { "path": "agents.3.name.given", "from": null, "to": "Rahul" }, { "path": "agents.3.role", "from": null, "to": "author" }, { "path": "agents.3.sequence", "from": null, "to": "additional" }, { "path": "agents.4.name.family", "from": null, "to": "Ragasa Gutata" }, { "path": "agents.4.name.given", "from": null, "to": "Gamachis" }, { "path": "agents.4.role", "from": null, "to": "author" }, { "path": "agents.4.sequence", "from": null, "to": "additional" }, { "path": "agents.5.name.org", "from": null, "to": "Longman Publishers" }, { "path": "agents.5.role", "from": null, "to": "publisher" }, { "path": "characters.0", "from": null, "to": "Language" }, { "path": "container.identifiers.0.medium", "from": null, "to": "electronic" }, { "path": "container.identifiers.0.type", "from": null, "to": "ISSN" }, { "path": "container.identifiers.0.value", "from": null, "to": "2456-5660" }, { "path": "container.issue", "from": null, "to": "07" }, { "path": "container.pages.first", "from": null, "to": "29" }, { "path": "container.pages.last", "from": null, "to": "36" }, { "path": "container.titles.0.type", "from": null, "to": "PrincipalTitle" }, { "path": "container.titles.0.value", "from": null, "to": "Journal of Science & Technology" }, { "path": "container.type", "from": null, "to": "Journal" }, { "path": "container.volume", "from": null, "to": "10" }, { "path": "dates.date_type", "from": null, "to": "PublicationDate" }, { "path": "dates.online", "from": null, "to": "2025-07-23" }, { "path": "dates.published", "from": null, "to": "2025-07-23" }, { "path": "doi", "from": null, "to": "10.46243/jst.2025.v10.i07.pp29-36" }, { "path": "format", "from": null, "to": "smartscholars-doi-metadata/1.0" }, { "path": "identifiers.0.type", "from": null, "to": "DOI" }, { "path": "identifiers.0.value", "from": null, "to": "10.46243/jst.2025.v10.i07.pp29-36" }, { "path": "language", "from": null, "to": "en" }, { "path": "license.applies_to", "from": null, "to": "vor" }, { "path": "license.start", "from": null, "to": "2025-07-23" }, { "path": "license.url", "from": null, "to": "https://creativecommons.org/licenses/by/4.0/" }, { "path": "links.0.primary", "from": null, "to": true }, { "path": "links.0.return_type", "from": null, "to": "text/html" }, { "path": "links.0.url", "from": null, "to": "https://www.jst.org.in/index.php/pub/article/view/1432" }, { "path": "links.1.purpose", "from": null, "to": "text-mining" }, { "path": "links.1.return_type", "from": null, "to": "application/pdf" }, { "path": "links.1.url", "from": null, "to": "https://www.jst.org.in/index.php/pub/article/download/1432/1028" }, { "path": "links.2.purpose", "from": null, "to": "text-mining" }, { "path": "links.2.return_type", "from": null, "to": "application/xml" }, { "path": "links.2.url", "from": null, "to": "https://www.jst.org.in/index.php/pub/article/download/1432/1220" }, { "path": "links.3.purpose", "from": null, "to": "similarity-checking" }, { "path": "links.3.url", "from": null, "to": "https://jst.org.in/index.php/pub/article/view/1432/1028" }, { "path": "modes.0", "from": null, "to": "Visual" }, { "path": "record.issue_number", "from": null, "to": 1 }, { "path": "record.registered", "from": null, "to": "2026-08-27" }, { "path": "record.registrant", "from": null, "to": "Longman Publishers" }, { "path": "record.source", "from": null, "to": "crossref-api" }, { "path": "record.source_agency", "from": null, "to": "Crossref (member 25296)" }, { "path": "record.updated", "from": null, "to": "2026-09-20" }, { "path": "references.0.doi", "from": null, "to": "10.1016/j.bushor.2022.10.002" }, { "path": "references.0.key", "from": null, "to": "ref1" }, { "path": "references.0.unstructured", "from": null, "to": "F. 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FL will most likely provide a solution to the distance and make things clear in collaboration without exposing raw information from a consortium to boost its privacy. However, existing FL solutions include such challenges as increased overhead communication, risk in leaking data, and even the inefficiency of secure aggregation.To mitigate these constraints, this research proposes the Autoencoder-Based Federated Learning framework by integrating prevailing techniques such as differential privacy and homomorphic encryption that safeguard both the security and efficiency of the model. This method does not only steal model ideas for autoencoders to compress before sciences transmission but hugely reduces the transmission bandwidth and possibly minimizes gradient leakage. However, adaptive normalization is used to handle institutional heterogeneity to maintain better performance for the model. Conclusion of experimentation indicated that this framework could significantly reduce communication overhead while retaining high federated learning accuracy and even better security. Further, the trust-based client evaluation mechanism is presented to detect malicious behavior and improve reliability regarding federated aggregation. The experiment showed that Autoencoder Based Federated Learning was a scalable, secure, and privacy-efficient solution to applications tailored for healthcare, finance, and other sensitive data environments.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2025-07-23", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1016/j.bushor.2022.10.002", "unstructured": "F. Schäfer, H. Gebauer, C. Gröger, O. Gassmann, and F. 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Sinha, “Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions,” World J Gastroenterol, vol. 27, no. 17, pp. 1920–1935, May 2021, doi: 10.3748/wjg.v27. i17.1920" }, { "key": "ref25", "doi": "10.1109/access.2020.3021508", "unstructured": "L. Aziz, Md. S. B. Haji Salam, U. U. Sheikh, and S. Ayub, “Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review,” IEEE Access, vol. 8, pp. 170461–170495, 2020, doi: 10.1109/ ACCESS.2020.3021508" }, { "key": "ref26", "doi": "10.21917/ijivp.2025.0495", "unstructured": "Rathinam College of Arts and Science, India, G. R. N, K. 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Lakehal, “Blockchain based context-aware CP-ABE schema for Guman Singh Chauhan, Venkata Surya Teja Gollapalli, Kannan Srinivasan, Rahul Jadon, Gamachis Ragasa Gutata: Privacy and Data Protection: Ensuring Compliance with Federated Learning in the Digital Age Internet of Medical Things security,” Array, vol. 14, p. 100150, Jul. 2022, doi: 10.1016/j. array.2022.100150" }, { "key": "ref29", "doi": "10.1186/s13673-020-00219-9", "unstructured": "D. Cao, Z. Chen, and L. Gao, “An improved object detection algorithm based on multi-scaled and deformable convolutional neural networks,” Hum. Cent. Comput. Inf. Sci., vol. 10, no. 1, p. 14, Apr. 2020, doi: 10.1186/s13673-020- 00219-9" }, { "key": "ref30", "unstructured": "“A Survey on Supply Chain Management: Exploring Physical and Cyber Security Challenges, Threats, Critical Applications, and Innovative Technologies-ProQuest.” Accessed: Nov. 17, 2025. [Online]. 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FL will most likely provide a solution to the distance and make things clear in collaboration without exposing raw information from a consortium to boost its privacy. However, existing FL solutions include such challenges as increased overhead communication, risk in leaking data, and even the inefficiency of secure aggregation.To mitigate these constraints, this research proposes the Autoencoder-Based Federated Learning framework by integrating prevailing techniques such as differential privacy and homomorphic encryption that safeguard both the security and efficiency of the model. This method does not only steal model ideas for autoencoders to compress before sciences transmission but hugely reduces the transmission bandwidth and possibly minimizes gradient leakage. However, adaptive normalization is used to handle institutional heterogeneity to maintain better performance for the model. Conclusion of experimentation indicated that this framework could significantly reduce communication overhead while retaining high federated learning accuracy and even better security. Further, the trust-based client evaluation mechanism is presented to detect malicious behavior and improve reliability regarding federated aggregation. The experiment showed that Autoencoder Based Federated Learning was a scalable, secure, and privacy-efficient solution to applications tailored for healthcare, finance, and other sensitive data environments.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2025-07-23", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1016/j.bushor.2022.10.002", "unstructured": "F. Schäfer, H. Gebauer, C. Gröger, O. Gassmann, and F. 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Talesh, “Data Breach, Privacy, and Cyber Insurance: How Insurance Companies Act as ‘Compliance Managers’ for Businesses,” Law & Social Inquiry, vol. 43, no. 2, pp. 417–440, Apr. 2018, doi: 10.1111/lsi.12303" }, { "key": "ref9", "doi": "10.1002/widm.1211", "unstructured": "L. Cheng, F. Liu, and D. (Daphne) Yao, “Enterprise data breach: causes, challenges, Guman Singh Chauhan, Venkata Surya Teja Gollapalli, Kannan Srinivasan, Rahul Jadon, Gamachis Ragasa Gutata: Privacy and Data Protection: Ensuring Compliance with Federated Learning in the Digital Age prevention, and future directions,” WIREs Data Mining and Knowledge Discovery, vol. 7, no. 5, p. e1211, 2017, doi: 10.1002/widm.1211" }, { "key": "ref10", "doi": "10.1177/1354856516640710", "unstructured": "T. Jordan, “A genealogy of hacking,” Convergence, vol. 23, no. 5, pp. 528–544, Oct. 2017, doi: 10.1177/1354856516640710" }, { "key": "ref11", "unstructured": "M. S. C. Mansor and M. F. 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