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ACM, 2011. https://ieeexplore.ieee.org/ document/9887796/" }, { "path": "referent", "from": null, "to": "Creation" }, { "path": "structural_type", "from": null, "to": "Digital" }, { "path": "titles.0.lang", "from": null, "to": "en" }, { "path": "titles.0.type", "from": null, "to": "PrincipalTitle" }, { "path": "titles.0.value", "from": null, "to": "A Machine Learning Framework For Data Poisoning Attacks" }, { "path": "type", "from": null, "to": "JournalArticle" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2023.v8.i07.pp169-176", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "A Machine Learning Framework For Data Poisoning Attacks", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2023.v8.i07.pp169-176" } ], "agents": [ { "role": "author", "name": { "given": "Priyanka Narsingoju", "family": "Priyanka Narsingoju" }, "sequence": "first" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2023-08-07", "date_type": "PublicationDate", "online": "2023-08-07" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "8", "issue": "7", "pages": { "first": "169", "last": "176" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/765", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/765/693", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/765/1739", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://jst.org.in/admin/uploads/PRIYA,21S41F0041.pdf", "purpose": "similarity-checking" } ], "abstract": { "value": "Federated models are built by collecting model changes from participants. 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