{"id":"https://openalex.org/W4310969328","doi":"https://doi.org/10.1109/iecon49645.2022.9968358","title":"Fake News Detection using a Decentralized Deep Learning Model and Federated Learning","display_name":"Fake News Detection using a Decentralized Deep Learning Model and Federated Learning","publication_year":2022,"publication_date":"2022-10-17","ids":{"openalex":"https://openalex.org/W4310969328","doi":"https://doi.org/10.1109/iecon49645.2022.9968358"},"language":"en","primary_location":{"id":"doi:10.1109/iecon49645.2022.9968358","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon49645.2022.9968358","pdf_url":null,"source":{"id":"https://openalex.org/S4363607717","display_name":"IECON 2022 \u2013 48th Annual Conference of the IEEE Industrial Electronics Society","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2022 \u2013 48th Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026149280","display_name":"Nirosh Jayakody","orcid":null},"institutions":[{"id":"https://openalex.org/I153230381","display_name":"Charles Sturt University","ror":"https://ror.org/00wfvh315","country_code":"AU","type":"education","lineage":["https://openalex.org/I153230381"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Nirosh Jayakody","raw_affiliation_strings":["Charles Sturt University,School of Computing and Mathematics,VIC,Australia,3000"],"affiliations":[{"raw_affiliation_string":"Charles Sturt University,School of Computing and Mathematics,VIC,Australia,3000","institution_ids":["https://openalex.org/I153230381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112890309","display_name":"Azeem Mohammad","orcid":null},"institutions":[{"id":"https://openalex.org/I153230381","display_name":"Charles Sturt University","ror":"https://ror.org/00wfvh315","country_code":"AU","type":"education","lineage":["https://openalex.org/I153230381"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Azeem Mohammad","raw_affiliation_strings":["Charles Sturt University,School of Computing and Mathematics,VIC,Australia,3000"],"affiliations":[{"raw_affiliation_string":"Charles Sturt University,School of Computing and Mathematics,VIC,Australia,3000","institution_ids":["https://openalex.org/I153230381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053757316","display_name":"Malka N. Halgamuge","orcid":"https://orcid.org/0000-0001-9994-3778"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Malka N. Halgamuge","raw_affiliation_strings":["RMIT University,Department of Information Systems and Business Analytics,Melbourne,VIC,Australia,3000"],"affiliations":[{"raw_affiliation_string":"RMIT University,Department of Information Systems and Business Analytics,Melbourne,VIC,Australia,3000","institution_ids":["https://openalex.org/I82951845"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5026149280"],"corresponding_institution_ids":["https://openalex.org/I153230381"],"apc_list":null,"apc_paid":null,"fwci":3.1982,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.93185185,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.9909999966621399,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7946397066116333},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.7235958576202393},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.6777281761169434},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.6704129576683044},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6703619956970215},{"id":"https://openalex.org/keywords/fake-news","display_name":"Fake news","score":0.6138999462127686},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5760776400566101},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.57205730676651},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.5396440625190735},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5252113938331604},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.47131142020225525},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4267062544822693},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.41529226303100586},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32360488176345825},{"id":"https://openalex.org/keywords/internet-privacy","display_name":"Internet privacy","score":0.17881086468696594},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.17455148696899414}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7946397066116333},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.7235958576202393},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.6777281761169434},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.6704129576683044},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6703619956970215},{"id":"https://openalex.org/C2779756789","wikidata":"https://www.wikidata.org/wiki/Q28549308","display_name":"Fake news","level":2,"score":0.6138999462127686},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5760776400566101},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.57205730676651},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.5396440625190735},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5252113938331604},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.47131142020225525},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4267062544822693},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.41529226303100586},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32360488176345825},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.17881086468696594},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.17455148696899414},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iecon49645.2022.9968358","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon49645.2022.9968358","pdf_url":null,"source":{"id":"https://openalex.org/S4363607717","display_name":"IECON 2022 \u2013 48th Annual Conference of the IEEE Industrial Electronics Society","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2022 \u2013 48th Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2993771096","https://openalex.org/W3010624963","https://openalex.org/W3022024046","https://openalex.org/W3031885913","https://openalex.org/W3117687414","https://openalex.org/W3119467012","https://openalex.org/W3128412913","https://openalex.org/W3137820162","https://openalex.org/W3146371923","https://openalex.org/W3148001275","https://openalex.org/W3153854677","https://openalex.org/W3164279875","https://openalex.org/W3168667219","https://openalex.org/W3175501901","https://openalex.org/W4318619660","https://openalex.org/W6728757088"],"related_works":["https://openalex.org/W4298221930","https://openalex.org/W2777914285","https://openalex.org/W2890339288","https://openalex.org/W4378677776","https://openalex.org/W2966672946","https://openalex.org/W3013363440","https://openalex.org/W4287823391","https://openalex.org/W3137554057","https://openalex.org/W2358294942","https://openalex.org/W4367460280"],"abstract_inverted_index":{"Social":[0,33],"media":[1,18],"has":[2,19,35],"beneficial":[3],"and":[4,39,105,115,197],"detrimental":[5],"impacts":[6],"on":[7,16],"social":[8,17],"life.":[9],"The":[10,101,157,188],"vast":[11],"distribution":[12],"of":[13,103,127,135,172,190,199],"false":[14,177],"information":[15],"become":[20],"a":[21,25,56,69,173],"worldwide":[22],"threat.":[23],"As":[24],"result,":[26],"the":[27,97,125,132,180,195],"Fake":[28],"News":[29],"Detection":[30],"System":[31],"in":[32,37,148],"Networks":[34],"risen":[36],"popularity":[38],"is":[40,108],"now":[41],"considered":[42],"an":[43,84],"emerging":[44],"research":[45],"area.":[46],"A":[47],"centralized":[48,106,174],"training":[49],"technique":[50,140,182],"makes":[51],"it":[52],"difficult":[53],"to":[54,95,167],"build":[55],"generalized":[57],"model":[58,73,166],"by":[59,123],"adapting":[60],"numerous":[61],"data":[62],"sources.":[63,201],"In":[64,118],"this":[65],"study,":[66],"we":[67,163],"develop":[68],"decentralized":[70,104,138],"Deep":[71],"Learning":[72,76],"using":[74,111],"Federated":[75],"(FL)":[77],"for":[78,176],"fake":[79,86],"news":[80,87,178,200],"detection.":[81],"We":[82,130],"utilize":[83],"ISOT":[85],"dataset":[88],"gathered":[89],"from":[90],"\"Reuters.com\"":[91],"(N":[92],"=":[93],"44,898)":[94],"train":[96],"deep":[98],"learning":[99],"model.":[100],"performance":[102,120],"models":[107],"then":[109],"assessed":[110],"accuracy,":[112],"precision,":[113],"recall,":[114],"F1-score":[116],"measures.":[117],"addition,":[119],"was":[121],"measured":[122],"varying":[124],"number":[126],"FL":[128,139,181],"clients.":[129],"identify":[131],"high":[133],"accuracy":[134],"our":[136,165],"proposed":[137],"(accuracy,":[141],"99.6%)":[142],"utilizing":[143],"fewer":[144],"communication":[145],"rounds":[146],"than":[147],"previous":[149],"studies,":[150],"even":[151],"without":[152],"employing":[153],"pre-trained":[154],"word":[155],"embedding.":[156],"highest":[158],"effects":[159],"are":[160],"obtained":[161],"when":[162],"compare":[164],"three":[168],"earlier":[169],"research.":[170],"Instead":[171],"method":[175],"detection,":[179],"may":[183],"be":[184],"used":[185],"more":[186],"efficiently.":[187],"use":[189],"Blockchain-like":[191],"technologies":[192],"can":[193],"improve":[194],"integrity":[196],"validity":[198]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}