{"id":"https://openalex.org/W4315629742","doi":"https://doi.org/10.1109/globecom48099.2022.10000962","title":"Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange","display_name":"Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange","publication_year":2022,"publication_date":"2022-12-04","ids":{"openalex":"https://openalex.org/W4315629742","doi":"https://doi.org/10.1109/globecom48099.2022.10000962"},"language":"en","primary_location":{"id":"doi:10.1109/globecom48099.2022.10000962","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/globecom48099.2022.10000962","pdf_url":null,"source":{"id":"https://openalex.org/S4363607705","display_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","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":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","raw_type":"proceedings-article"},"type":"conference-paper","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/A5037239032","display_name":"Satyavrat Wagle","orcid":"https://orcid.org/0009-0004-0153-225X"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Satyavrat Wagle","raw_affiliation_strings":["School of Electrical and Computer Engineering, Purdue University,West Lafayette,IN,USA","School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University,West Lafayette,IN,USA","institution_ids":["https://openalex.org/I219193219"]},{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059750214","display_name":"Seyyedali Hosseinalipour","orcid":"https://orcid.org/0000-0003-4266-4000"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Seyyedali Hosseinalipour","raw_affiliation_strings":["School of Electrical and Computer Engineering, Purdue University,West Lafayette,IN,USA","School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University,West Lafayette,IN,USA","institution_ids":["https://openalex.org/I219193219"]},{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046661187","display_name":"Naji Khosravan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210149135","display_name":"Zillow Group (United States)","ror":"https://ror.org/03n86v874","country_code":"US","type":"company","lineage":["https://openalex.org/I4210149135"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Naji Khosravan","raw_affiliation_strings":["Zillow Group,Seattle,WA,USA","Zillow Group, Seattle, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zillow Group,Seattle,WA,USA","institution_ids":["https://openalex.org/I4210149135"]},{"raw_affiliation_string":"Zillow Group, Seattle, WA, USA","institution_ids":["https://openalex.org/I4210149135"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110782105","display_name":"Mung Chiang","orcid":"https://orcid.org/0000-0002-8920-651X"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mung Chiang","raw_affiliation_strings":["School of Electrical and Computer Engineering, Purdue University,West Lafayette,IN,USA","School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University,West Lafayette,IN,USA","institution_ids":["https://openalex.org/I219193219"]},{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020399355","display_name":"Christopher G. Brinton","orcid":"https://orcid.org/0000-0003-2771-3521"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christopher G. Brinton","raw_affiliation_strings":["School of Electrical and Computer Engineering, Purdue University,West Lafayette,IN,USA","School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University,West Lafayette,IN,USA","institution_ids":["https://openalex.org/I219193219"]},{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9873999953269958,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9408000111579895,"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":0.8126707077026367},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.7839261293411255},{"id":"https://openalex.org/keywords/independent-and-identically-distributed-random-variables","display_name":"Independent and identically distributed random variables","score":0.6825222969055176},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.6662489175796509},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.6145275235176086},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5908723473548889},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5191906690597534},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5132186412811279},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.5070652961730957},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.48984023928642273},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.4769921898841858},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4761602580547333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.445936918258667},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4390895962715149},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"Distributed learning","score":0.42563578486442566},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.10657799243927002},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.09138122200965881}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8126707077026367},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7839261293411255},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.6825222969055176},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.6662489175796509},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.6145275235176086},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5908723473548889},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5191906690597534},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5132186412811279},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.5070652961730957},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.48984023928642273},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.4769921898841858},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4761602580547333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.445936918258667},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4390895962715149},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.42563578486442566},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.10657799243927002},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.09138122200965881},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom48099.2022.10000962","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/globecom48099.2022.10000962","pdf_url":null,"source":{"id":"https://openalex.org/S4363607705","display_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","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":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals","score":0.5600000023841858}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2073459066","https://openalex.org/W2138621090","https://openalex.org/W2750384547","https://openalex.org/W2807006176","https://openalex.org/W2894176037","https://openalex.org/W2936286953","https://openalex.org/W2963318081","https://openalex.org/W2995022099","https://openalex.org/W3021654819","https://openalex.org/W3026182744","https://openalex.org/W3035524453","https://openalex.org/W3047304572","https://openalex.org/W3091635927","https://openalex.org/W3093331990","https://openalex.org/W3094368349","https://openalex.org/W3121122428","https://openalex.org/W3131394593","https://openalex.org/W3155160971","https://openalex.org/W3163584387","https://openalex.org/W3182158470","https://openalex.org/W3204183032","https://openalex.org/W4383750252","https://openalex.org/W4386453702","https://openalex.org/W6668990524","https://openalex.org/W6752029299","https://openalex.org/W6774314701","https://openalex.org/W6809950369"],"related_works":["https://openalex.org/W4317941881","https://openalex.org/W4323521275","https://openalex.org/W2998530156","https://openalex.org/W3035996294","https://openalex.org/W2954034773","https://openalex.org/W3013510494","https://openalex.org/W4229067761","https://openalex.org/W3091296419","https://openalex.org/W4385893187","https://openalex.org/W4320067866"],"abstract_inverted_index":{"Federated":[0,63],"learning":[1,16],"(FL)":[2],"has":[3,25],"been":[4,26],"recognized":[5],"as":[6,132],"one":[7],"of":[8,20,49,125,139,159],"the":[9,21,186],"most":[10,19],"promising":[11],"solutions":[12],"for":[13,28,68,171],"distributed":[14,101],"machine":[15],"(ML).":[17],"In":[18],"current":[22],"literature,":[23],"FL":[24,69,116],"studied":[27],"supervised":[29],"ML":[30],"tasks,":[31],"in":[32,40,118,181],"which":[33],"edge":[34,71],"devices":[35,72,86],"collect":[36],"labeled":[37,50],"data.":[38],"Nevertheless,":[39],"many":[41],"applications,":[42],"it":[43],"is":[44,179],"impractical":[45],"to":[46,91,114,129,156],"assume":[47],"existence":[48],"data":[51,82,110],"across":[52,70,164,185],"devices.":[53,187],"To":[54],"this":[55],"end,":[56],"we":[57],"develop":[58],"a":[59,107,123,137,146],"novel":[60],"methodology,":[61],"Cooperative":[62],"unsupervised":[64,115,160],"Contrastive":[65],"Learning":[66],"(CF-CL),":[67],"with":[73],"unlabeled":[74],"datasets.":[75,104],"CF-CL":[76,105,154],"employs":[77],"local":[78,93,103,127],"device":[79,121],"cooperation":[80],"where":[81],"are":[83],"exchanged":[84],"among":[85],"through":[87,145],"device-to-device":[88],"(D2D)":[89],"communications":[90],"avoid":[92],"model":[94,175],"bias":[95],"resulting":[96],"from":[97,141],"non-independent":[98],"and":[99,135,177],"identically":[100],"(non-i.i.d.)":[102],"introduces":[106],"push-pull":[108],"smart":[109],"sharing":[111],"mechanism":[112],"tailored":[113],"settings,":[117],"which,":[119],"each":[120],"pushes":[122],"subset":[124],"its":[126,130,142],"datapoints":[128,140],"neighbors":[131],"reserved":[133],"datapoints,":[134],"pulls":[136],"set":[138],"neighbors,":[143],"sampled":[144],"probabilistic":[147],"importance":[148],"sampling":[149],"technique.":[150],"We":[151],"demonstrate":[152],"that":[153],"leads":[155],"(i)":[157],"alignment":[158],"learned":[161],"latent":[162],"spaces":[163],"devices,":[165],"(ii)":[166],"faster":[167],"global":[168,174],"convergence,":[169],"allowing":[170],"less":[172],"frequent":[173],"aggregations;":[176],"(iii)":[178],"effective":[180],"extreme":[182],"non-i.i.d.":[183],"datasettings":[184]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
