{"id":"https://openalex.org/W7141405957","doi":"https://doi.org/10.48550/arxiv.2603.24601","title":"FED-HARGPT: A Hybrid Centralized-Federated Approach of a Transformer-based Architecture for Human Context Recognition","display_name":"FED-HARGPT: A Hybrid Centralized-Federated Approach of a Transformer-based Architecture for Human Context Recognition","publication_year":2026,"publication_date":"2026-03-13","ids":{"openalex":"https://openalex.org/W7141405957","doi":"https://doi.org/10.48550/arxiv.2603.24601"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.24601","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24601","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.24601","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112885509","display_name":"Wandemberg Gibaut","orcid":"https://orcid.org/0000-0001-7439-9788"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gibaut, Wandemberg","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113976393","display_name":"Alexandre Osorio","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Osorio, Alexandre","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130764405","display_name":"Amparo Munoz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Munoz, Amparo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114100888","display_name":"Sildolfo F. G. Neto","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Neto, Sildolfo F. G.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5051614068","display_name":"Fabio Grassiotto","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Grassiotto, Fabio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5112885509"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.8790000081062317,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.8790000081062317,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.024000000208616257,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.01140000019222498,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.720300018787384},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.697700023651123},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6270999908447266},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.47839999198913574},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4618000090122223},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.44670000672340393},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.4255000054836273},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.4027999937534332},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3779999911785126}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7698000073432922},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.720300018787384},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.697700023651123},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6270999908447266},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.47839999198913574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4781999886035919},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4618000090122223},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.44670000672340393},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.4255000054836273},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.4027999937534332},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4011000096797943},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3779999911785126},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3734999895095825},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.3686000108718872},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3562000095844269},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.34779998660087585},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.33219999074935913},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.3240000009536743},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.3075000047683716},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.29580000042915344},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.2808000147342682},{"id":"https://openalex.org/C47487241","wikidata":"https://www.wikidata.org/wiki/Q5227230","display_name":"Data access","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.2646999955177307}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.24601","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24601","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.24601","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24601","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,84,113],"study":[1],"explores":[2],"a":[3,13,28,76,81,109,131],"hybrid":[4,93],"centralized-federated":[5],"approach":[6,94],"for":[7],"Human":[8],"Activity":[9],"Recognition":[10],"(HAR)":[11],"using":[12,59],"Transformer-based":[14],"architecture.":[15],"With":[16],"the":[17,68,73,88,91,97,124],"increasing":[18],"ubiquity":[19],"of":[20,31,44,75,90,101,126],"edge":[21],"devices,":[22],"such":[23],"as":[24],"smartphones":[25],"and":[26,36,50,63,99,136],"wearables,":[27],"significant":[29],"amount":[30],"private":[32],"data":[33,62,106,111,134],"from":[34,80],"wearable":[35],"inertial":[37],"sensors":[38],"is":[39],"generated,":[40],"facilitating":[41],"discreet":[42],"monitoring":[43],"human":[45],"activities,":[46],"including":[47],"resting,":[48],"sleeping,":[49],"walking.":[51],"This":[52],"research":[53],"focuses":[54],"on":[55],"deploying":[56],"HAR":[57,102],"technologies":[58],"mobile":[60],"sensor":[61],"leveraging":[64],"Federated":[65],"Learning":[66],"within":[67],"Flower":[69],"framework":[70],"to":[71,120,129],"evaluate":[72],"training":[74],"federated":[77,114,127],"model":[78,137],"derived":[79],"centralized":[82,121],"baseline.":[83],"experimental":[85],"results":[86],"demonstrate":[87],"effectiveness":[89],"proposed":[92],"in":[95,108,139],"improving":[96],"accuracy":[98],"robustness":[100],"models":[103],"while":[104],"preserving":[105],"privacy":[107,135],"non-IID":[110],"scenario.":[112],"learning":[115,128],"setup":[116],"demonstrated":[117],"comparable":[118],"performance":[119,138],"models,":[122],"highlighting":[123],"potential":[125],"strike":[130],"balance":[132],"between":[133],"real-world":[140],"applications.":[141]},"counts_by_year":[],"updated_date":"2026-04-30T09:15:22.047038","created_date":"2026-03-28T00:00:00"}
