{"id":"https://openalex.org/W7167287281","doi":"https://doi.org/10.48550/arxiv.2607.02426","title":"QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition","display_name":"QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition","publication_year":2026,"publication_date":"2026-07-02","ids":{"openalex":"https://openalex.org/W7167287281","doi":"https://doi.org/10.48550/arxiv.2607.02426"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.02426","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.02426","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.02426","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139959543","display_name":"Quoc Bao Phan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Phan, Quoc Bao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5055619213","display_name":"Tuy Tan Nguyen","orcid":"https://orcid.org/0000-0002-9485-7720"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Tuy Tan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.24289999902248383,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.24289999902248383,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T13553","display_name":"Age of Information Optimization","score":0.04349999874830246,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.04309999942779541,"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/overhead","display_name":"Overhead (engineering)","score":0.6158000230789185},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.576200008392334},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5156999826431274},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.47929999232292175},{"id":"https://openalex.org/keywords/independent-and-identically-distributed-random-variables","display_name":"Independent and identically distributed random variables","score":0.43630000948905945},{"id":"https://openalex.org/keywords/quantum","display_name":"Quantum","score":0.42500001192092896},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4189999997615814}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7479000091552734},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6158000230789185},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.576200008392334},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5156999826431274},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.47929999232292175},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.43630000948905945},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.43380001187324524},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.42500001192092896},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4189999997615814},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.41589999198913574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3700999915599823},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.30970001220703125},{"id":"https://openalex.org/C2781234732","wikidata":"https://www.wikidata.org/wiki/Q943505","display_name":"Fusion center","level":4,"score":0.3075000047683716},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30329999327659607},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C58053490","wikidata":"https://www.wikidata.org/wiki/Q176555","display_name":"Quantum computer","level":3,"score":0.2870999872684479},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25949999690055847},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.25859999656677246}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.02426","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.02426","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.02426","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.02426","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Federated":[0],"learning":[1],"(FL)":[2],"enables":[3],"collaborative":[4],"model":[5],"training":[6],"across":[7],"distributed":[8,31],"devices":[9],"without":[10],"sharing":[11],"raw":[12],"data,":[13],"making":[14],"it":[15],"suitable":[16],"for":[17,62],"privacy-sensitive":[18],"robotic":[19],"sensing":[20],"applications.":[21],"However,":[22],"multi-agent":[23,63],"systems":[24],"generate":[25],"heterogeneous":[26],"and":[27,29,49,83],"non-independent":[28],"identically":[30],"(non-IID)":[32],"multimodal":[33],"sensor":[34],"streams":[35],"that":[36,75,119],"degrade":[37],"conventional":[38,126],"FL":[39,60],"algorithms,":[40],"while":[41],"classical":[42,94],"fusion":[43,73,122],"modules":[44],"introduce":[45],"substantial":[46],"parameter":[47,102],"overhead":[48],"communication":[50],"cost.":[51],"This":[52],"paper":[53],"proposes":[54],"QFedAgent,":[55],"a":[56,69],"hybrid":[57],"quantum-classical":[58],"personalized":[59],"framework":[61],"activity":[64],"recognition.":[65],"The":[66],"approach":[67],"integrates":[68],"variational":[70],"quantum":[71,80,88,121],"circuit":[72],"module":[74],"models":[76],"accelerometer--gyroscope":[77],"interactions":[78],"through":[79],"state":[81],"encoding":[82],"entanglement,":[84],"requiring":[85],"only":[86],"72":[87],"rotation":[89],"parameters":[90],"versus":[91],"33K":[92],"in":[93],"multi-layer":[95],"perceptron-based":[96],"fusion,":[97],"achieving":[98],"approximately":[99],"10x":[100],"total":[101],"reduction.":[103],"Experiments":[104],"on":[105],"the":[106],"OPPORTUNITY":[107],"dataset":[108],"under":[109],"subject-based":[110],"non-IID":[111],"partitions":[112],"demonstrate":[113],"97.7%":[114],"mean":[115],"test":[116],"accuracy,":[117],"confirming":[118],"parameter-efficient":[120],"remains":[123],"competitive":[124],"with":[125],"federated":[127],"baselines.":[128]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-04T00:00:00"}