{"id":"https://openalex.org/W4407784196","doi":"https://doi.org/10.1109/comsnets63942.2025.10885671","title":"FedLabeling: A Federated Framework for Handling Noisy Labeled Data for Efficient Alarm Detection","display_name":"FedLabeling: A Federated Framework for Handling Noisy Labeled Data for Efficient Alarm Detection","publication_year":2025,"publication_date":"2025-01-06","ids":{"openalex":"https://openalex.org/W4407784196","doi":"https://doi.org/10.1109/comsnets63942.2025.10885671"},"language":"en","primary_location":{"id":"doi:10.1109/comsnets63942.2025.10885671","is_oa":false,"landing_page_url":"https://doi.org/10.1109/comsnets63942.2025.10885671","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS)","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/A5019643071","display_name":"Satheesh K. Perepu","orcid":"https://orcid.org/0000-0002-5132-2144"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Satheesh K. Perepu","raw_affiliation_strings":["Ericsson Research,Chennai,India"],"affiliations":[{"raw_affiliation_string":"Ericsson Research,Chennai,India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100723513","display_name":"M. Saravanan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"M. Saravanan","raw_affiliation_strings":["Ericsson Research,Chennai,India"],"affiliations":[{"raw_affiliation_string":"Ericsson Research,Chennai,India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5019643071"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02960587,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"422","last_page":"429"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9757000207901001,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9757000207901001,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12761","display_name":"Data Stream Mining Techniques","score":0.965399980545044,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9580000042915344,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7978372573852539},{"id":"https://openalex.org/keywords/alarm","display_name":"ALARM","score":0.6780003309249878},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4017730951309204},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06969329714775085}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7978372573852539},{"id":"https://openalex.org/C2779119184","wikidata":"https://www.wikidata.org/wiki/Q294350","display_name":"ALARM","level":2,"score":0.6780003309249878},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4017730951309204},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06969329714775085},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/comsnets63942.2025.10885671","is_oa":false,"landing_page_url":"https://doi.org/10.1109/comsnets63942.2025.10885671","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS)","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":24,"referenced_works":["https://openalex.org/W2596142952","https://openalex.org/W3042609801","https://openalex.org/W3091870957","https://openalex.org/W3110845456","https://openalex.org/W3130042795","https://openalex.org/W3139463697","https://openalex.org/W3197301256","https://openalex.org/W3207527102","https://openalex.org/W4224272230","https://openalex.org/W4285787895","https://openalex.org/W4312231739","https://openalex.org/W4312699393","https://openalex.org/W4387460392","https://openalex.org/W4392357773","https://openalex.org/W6693514096","https://openalex.org/W6728757088","https://openalex.org/W6762840122","https://openalex.org/W6767228950","https://openalex.org/W6768632158","https://openalex.org/W6771630921","https://openalex.org/W6775567351","https://openalex.org/W6787972765","https://openalex.org/W6798660638","https://openalex.org/W6837974329"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2731305060","https://openalex.org/W2372003537","https://openalex.org/W2732807254","https://openalex.org/W2587670262","https://openalex.org/W3091941553","https://openalex.org/W3037375888","https://openalex.org/W2366730739"],"abstract_inverted_index":{"Federated":[0],"learning":[1,6,122],"(FL)":[2],"is":[3],"a":[4,91],"collaborative":[5],"method":[7,92,110],"that":[8],"allows":[9],"models":[10],"to":[11,17,47,76,106,134,163],"be":[12],"trained":[13],"without":[14,103],"requiring":[15],"users":[16,44,69,75],"transfer":[18],"their":[19,83,107],"data.":[20,84,108],"However,":[21],"traditional":[22,164],"FL":[23],"methods":[24,28,42],"assume":[25,67],"consistent":[26],"labeling":[27,41],"across":[29,43],"all":[30],"users.":[31],"In":[32],"real-time":[33],"applications,":[34],"especially":[35],"in":[36,50,82],"the":[37,57,78,97,120,127,136,150,154],"telecommunications":[38],"sector,":[39],"different":[40],"may":[45],"lead":[46],"label":[48,79,137],"noise":[49,80,98,138],"each":[51,101,112],"user\u2019s":[52,113],"data,":[53],"which":[54,95],"can":[55],"impact":[56],"overall":[58],"model":[59,117,156],"performance.To":[60],"address":[61],"this":[62],"issue,":[63],"existing":[64],"approaches":[65],"either":[66],"some":[68],"have":[70],"noise-free":[71],"labels":[72],"or":[73],"require":[74],"report":[77],"levels":[81],"To":[85],"circumvent":[86],"these":[87],"limitations,":[88],"we":[89],"proposed":[90,160],"called":[93],"\"FedLabeling,\"":[94],"estimates":[96,111],"level":[99],"at":[100],"user":[102],"needing":[104],"access":[105],"Our":[109],"data":[114,129,133],"impressions":[115],"from":[116,144],"updates":[118],"using":[119,158],"zero-shot":[121],"concept":[123],"and":[124,146],"then":[125],"compares":[126],"estimated":[128],"with":[130],"publicly":[131],"available":[132],"estimate":[135],"level.Results":[139],"on":[140],"five":[141],"benchmark":[142],"datasets":[143],"telecom":[145],"non-telecom":[147],"domains":[148],"demonstrate":[149],"improved":[151],"performance":[152],"of":[153],"global":[155],"obtained":[157],"our":[159],"approach":[161],"compared":[162],"methods.":[165]},"counts_by_year":[],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}
