{"id":"https://openalex.org/W4400102012","doi":"https://doi.org/10.1016/j.measen.2024.101268","title":"A lightweight and privacy preserved federated learning ecosystem for analyzing verbal communication emotions in identical and non-identical databases","display_name":"A lightweight and privacy preserved federated learning ecosystem for analyzing verbal communication emotions in identical and non-identical databases","publication_year":2024,"publication_date":"2024-06-26","ids":{"openalex":"https://openalex.org/W4400102012","doi":"https://doi.org/10.1016/j.measen.2024.101268"},"language":"en","primary_location":{"id":"doi:10.1016/j.measen.2024.101268","doi":"https://doi.org/10.1016/j.measen.2024.101268","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.measen.2024.101268","pdf_url":null,"source":{"id":"https://openalex.org/S4210238442","display_name":"Measurement Sensors","issn_l":"2665-9174","issn":["2665-9174"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1","poland-20"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Measurement: Sensors","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1016/j.measen.2024.101268","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044948935","display_name":"Muskan Chawla","orcid":"https://orcid.org/0000-0001-9318-2971","observed_orcids":["https://orcid.org/0000-0001-9318-2971"]},"institutions":[{"id":"https://openalex.org/I74319210","display_name":"Chitkara University","ror":"https://ror.org/057d6z539","country_code":"IN","type":"education","lineage":["https://openalex.org/I74319210"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Muskan Chawla","raw_affiliation_strings":["Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India","institution_ids":["https://openalex.org/I74319210"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102872957","display_name":"Surya Narayan Panda","orcid":"https://orcid.org/0000-0001-5958-4811","observed_orcids":["https://orcid.org/0000-0001-5958-4811"]},"institutions":[{"id":"https://openalex.org/I74319210","display_name":"Chitkara University","ror":"https://ror.org/057d6z539","country_code":"IN","type":"education","lineage":["https://openalex.org/I74319210"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Surya Narayan Panda","raw_affiliation_strings":["Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India","institution_ids":["https://openalex.org/I74319210"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043722627","display_name":"Vikas Khullar","orcid":"https://orcid.org/0000-0002-0404-3652","observed_orcids":["https://orcid.org/0000-0002-0404-3652"]},"institutions":[{"id":"https://openalex.org/I74319210","display_name":"Chitkara University","ror":"https://ror.org/057d6z539","country_code":"IN","type":"education","lineage":["https://openalex.org/I74319210"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Vikas Khullar","raw_affiliation_strings":["Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India"],"raw_orcid":"https://orcid.org/0000-0002-0404-3652","affiliations":[{"raw_affiliation_string":"Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India","institution_ids":["https://openalex.org/I74319210"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033305490","display_name":"Sushil Kumar","orcid":"https://orcid.org/0000-0002-2708-7789","observed_orcids":["https://orcid.org/0000-0002-2708-7789"]},"institutions":[{"id":"https://openalex.org/I4210133503","display_name":"Central University of Haryana","ror":"https://ror.org/03mtwkv54","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210133503"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sushil Kumar","raw_affiliation_strings":["Department of Computer Science and Engineering, School of Engineering and Technology, Central University of Haryana, Mahendergarh, Haryana, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, School of Engineering and Technology, Central University of Haryana, Mahendergarh, Haryana, India","institution_ids":["https://openalex.org/I4210133503"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111097988","display_name":"Shyama Barna Bhattacharjee","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I91090688","display_name":"University of Science and Technology Chittagong","ror":"https://ror.org/00w9tx359","country_code":"BD","type":"education","lineage":["https://openalex.org/I91090688"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Shyama Barna Bhattacharjee","raw_affiliation_strings":["University of Science and Technology Chittagong (USTC), Bangladesh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology Chittagong (USTC), Bangladesh","institution_ids":["https://openalex.org/I91090688"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5043722627"],"corresponding_institution_ids":["https://openalex.org/I74319210"],"apc_list":{"value":1800,"currency":"USD","value_usd":1800},"apc_paid":{"value":1800,"currency":"USD","value_usd":1800},"fwci":0.9365,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.74265866,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":"34","issue":null,"first_page":"101268","last_page":"101268"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.8593999743461609,"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"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.8593999743461609,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.12449999898672104,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.004699999932199717,"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/federated-learning","display_name":"federated learning","score":0.9929999709129333},{"id":"https://openalex.org/keywords/vocal-emotion-recognition","display_name":"vocal emotion recognition","score":0.7699999809265137},{"id":"https://openalex.org/keywords/speech-emotion-classification","display_name":"speech emotion classification","score":0.7300000190734863},{"id":"https://openalex.org/keywords/non-iid-data","display_name":"non-IID data","score":0.828000009059906},{"id":"https://openalex.org/keywords/data-privacy","display_name":"data privacy","score":0.8069999814033508},{"id":"https://openalex.org/keywords/mel-frequency-cepstral-coefficients","display_name":"Mel-frequency cepstral coefficients","score":0.8899999856948853},{"id":"https://openalex.org/keywords/audio-features","display_name":"audio features","score":0.46000000834465027},{"id":"https://openalex.org/keywords/social-communication-disorder","display_name":"social communication disorder","score":0.7839999794960022},{"id":"https://openalex.org/keywords/ravdess","display_name":"RAVDESS","score":0.7559999823570251},{"id":"https://openalex.org/keywords/crema","display_name":"Crema","score":0.9079999923706055},{"id":"https://openalex.org/keywords/tess","display_name":"TESS","score":0.8960000276565552},{"id":"https://openalex.org/keywords/savee","display_name":"SAVEE","score":0.9990000128746033}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7937010526657104},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.7165869474411011},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5398186445236206},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4721626937389374},{"id":"https://openalex.org/C6438553","wikidata":"https://www.wikidata.org/wiki/Q1185804","display_name":"Affective computing","level":2,"score":0.44531020522117615},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4422633945941925},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4265863299369812},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.41902053356170654},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.23001998662948608}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1016/j.measen.2024.101268","doi":"https://doi.org/10.1016/j.measen.2024.101268","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.measen.2024.101268","pdf_url":null,"source":{"id":"https://openalex.org/S4210238442","display_name":"Measurement Sensors","issn_l":"2665-9174","issn":["2665-9174"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1","poland-20"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Measurement: Sensors","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:244b2bfd18aa4085a7a52f9f97006a4c","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/244b2bfd18aa4085a7a52f9f97006a4c","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Measurement: Sensors, Vol 34, Iss , Pp 101268- (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1016/j.measen.2024.101268","doi":"https://doi.org/10.1016/j.measen.2024.101268","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.measen.2024.101268","pdf_url":null,"source":{"id":"https://openalex.org/S4210238442","display_name":"Measurement Sensors","issn_l":"2665-9174","issn":["2665-9174"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1","poland-20"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Measurement: Sensors","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2140514912","https://openalex.org/W2514054471","https://openalex.org/W2793383796","https://openalex.org/W2887647147","https://openalex.org/W2932584472","https://openalex.org/W2964216663","https://openalex.org/W3012501605","https://openalex.org/W3047263036","https://openalex.org/W3080359515","https://openalex.org/W3086378696","https://openalex.org/W3086809868","https://openalex.org/W3100506742","https://openalex.org/W3103806448","https://openalex.org/W3111512201","https://openalex.org/W3134700509","https://openalex.org/W3152757486","https://openalex.org/W3176045131","https://openalex.org/W3215484246","https://openalex.org/W3216638653","https://openalex.org/W4214505042","https://openalex.org/W4220829848","https://openalex.org/W4221161839","https://openalex.org/W4226031303","https://openalex.org/W4234180224","https://openalex.org/W4308222714","https://openalex.org/W4312352743","https://openalex.org/W4390843497","https://openalex.org/W4391574995"],"related_works":["https://openalex.org/W7160524580","https://openalex.org/W7208421382","https://openalex.org/W4413086962","https://openalex.org/W4228996454","https://openalex.org/W7164527455","https://openalex.org/W3101275766","https://openalex.org/W4392209970","https://openalex.org/W4391574995","https://openalex.org/W4414299652","https://openalex.org/W4224916209"],"abstract_inverted_index":{"The":[0,14,32,59,77,89,106,151,256],"lack":[1],"of":[2,17,24,35,53,169,287],"vocal":[3,56],"emotional":[4,51,318],"expression":[5],"is":[6,38],"a":[7,41,63,190],"major":[8],"deficit":[9],"in":[10,230,320],"social":[11],"communication":[12,198],"disorders.":[13],"current":[15],"scenario":[16],"artificial":[18],"intelligence":[19],"focuses":[20],"on":[21,177,212],"collaborative":[22,64],"training":[23,72],"deep":[25,74,192],"learning":[26,70,75,172,222,289],"models":[27,161,292],"without":[28,303],"losing":[29,304],"data":[30,117,207,261],"privacy.":[31],"primary":[33],"objective":[34],"this":[36,187],"paper":[37,188],"to":[39,46,85,147,205,232,315],"propose":[40],"federated":[42,69,171,191,221,288],"learning-based":[43,193],"classification":[44,200],"model":[45,174,224,234,240],"identify":[47,316],"and":[48,65,114,136,179,183,252,276,295,322],"analyze":[49],"the":[50,73,115,142,167,170,213,218,220,239,281,285,309],"capabilities":[52],"individuals":[54,321],"with":[55,123,154,259,265,293],"emotion":[57,301],"deficits.":[58],"methodology":[60,79],"has":[61,119],"developed":[62],"privacy-preserved":[66],"approach":[67],"using":[68],"for":[71,196,329],"models.":[76],"proposed":[78,310],"utilizes":[80],"Mel-frequency":[81],"Cepstral":[82],"Coefficients":[83],"(MFCC)":[84],"preprocess":[86],"audio":[87,99,108,121,144,203,298],"recordings.":[88],"four":[90],"datasets":[91,204],"(RAVDESS,":[92],"CREMA,":[93],"TESS,":[94],"SAVEE)":[95],"including":[96],"emotion-based":[97],"classified":[98],"recordings":[100,109],"were":[101],"collected":[102,107],"from":[103,157,300],"open":[104],"sources.":[105],"are":[110],"3":[111],"s":[112],"each":[113],"total":[116],"set":[118],"668376":[120],"files":[122],"happy":[124],"-":[125,138],"175119":[126],"files,":[127,131,135],"sad":[128],"\u2013":[129,133],"172611":[130],"angry":[132],"176346":[134],"normal":[137],"144300":[139],"files.":[140],"Further,":[141,186],"input":[143],"was":[145,175],"pre-processed":[146],"generate":[148],"MFCC":[149],"features.":[150],"study":[152],"began":[153],"extracting":[155],"features":[156,299],"multiple":[158],"pre-trained":[159],"DL":[160,211],"as":[162],"its":[163],"base":[164,233],"model.":[165],"Then,":[166],"performance":[168],"(FL)":[173],"tested":[176],"independent":[178],"identically":[180,294],"distributed":[181,297],"(IID)":[182],"non-IID":[184,260],"data.":[185],"presents":[189],"multimodal":[194],"system":[195],"verbal":[197,317],"emotions":[199],"that":[201],"uses":[202],"meet":[206],"privacy":[208],"requirements":[209],"by":[210],"FL":[214,257],"ecosystem.":[215],"As":[216],"per":[217],"findings,":[219],"trained":[223,291],"provides":[225],"nearly":[226],"similar":[227],"parametric":[228,305],"results":[229,283],"comparison":[231],"training.":[235],"For":[236],"IID":[237],"data,":[238],"had":[241],"99.71":[242],"%":[243],"validation":[244,253,266],"accuracy,":[245],"precision":[246,270],"(99.73":[247],"%),":[248,251,269,272,275],"recall":[249,273],"(99.69":[250],"loss":[254,278],"(0.01).":[255],"architecture":[258],"outperformed":[262],"these":[263],"measures":[264],"accuracy":[267],"(99.97":[268,271,274],"least":[277],"(0).":[279],"Hence":[280],"acquired":[282],"support":[284,324],"utilization":[286],"ecosystem-based":[290],"non-identically":[296],"identification":[302],"results.":[306],"In":[307],"conclusion,":[308],"techniques":[311],"could":[312,323],"be":[313],"applied":[314],"deficits":[319],"developing":[325],"emerging":[326],"technological":[327],"interventions":[328],"their":[330],"well-being.":[331]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1}],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2024-06-28T00:00:00"}