{"id":"https://openalex.org/W3169848021","doi":"https://doi.org/10.1109/globecom46510.2021.9685320","title":"Wireless Federated Learning with Limited Communication and Differential Privacy","display_name":"Wireless Federated Learning with Limited Communication and Differential Privacy","publication_year":2021,"publication_date":"2021-12-01","ids":{"openalex":"https://openalex.org/W3169848021","doi":"https://doi.org/10.1109/globecom46510.2021.9685320","mag":"3169848021"},"language":"en","primary_location":{"id":"doi:10.1109/globecom46510.2021.9685320","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom46510.2021.9685320","pdf_url":null,"source":{"id":"https://openalex.org/S4363607714","display_name":"2021 IEEE Global Communications Conference (GLOBECOM)","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":"2021 IEEE Global Communications Conference (GLOBECOM)","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/A5020222911","display_name":"Amir Sonee","orcid":"https://orcid.org/0000-0002-0042-2751"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Amir Sonee","raw_affiliation_strings":["National Yang Ming Chiao Tung University (NYCU),Department of Electrical Engineering and Computer Science,Taiwan"],"affiliations":[{"raw_affiliation_string":"National Yang Ming Chiao Tung University (NYCU),Department of Electrical Engineering and Computer Science,Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067224441","display_name":"Stefano Rini","orcid":"https://orcid.org/0000-0003-1681-3316"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Stefano Rini","raw_affiliation_strings":["National Yang Ming Chiao Tung University (NYCU),Department of Electrical Engineering and Computer Science,Taiwan"],"affiliations":[{"raw_affiliation_string":"National Yang Ming Chiao Tung University (NYCU),Department of Electrical Engineering and Computer Science,Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082244371","display_name":"Yu-Chih Huang","orcid":"https://orcid.org/0000-0003-2135-1232"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yu-Chih Huang","raw_affiliation_strings":["National Yang Ming Chiao Tung University (NYCU),Department of Electrical Engineering and Computer Science,Taiwan"],"affiliations":[{"raw_affiliation_string":"National Yang Ming Chiao Tung University (NYCU),Department of Electrical Engineering and Computer Science,Taiwan","institution_ids":["https://openalex.org/I148366613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5020222911"],"corresponding_institution_ids":["https://openalex.org/I148366613"],"apc_list":null,"apc_paid":null,"fwci":0.8796,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.76509628,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"01","last_page":"06"},"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9732999801635742,"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/T10964","display_name":"Wireless Communication Security Techniques","score":0.9724000096321106,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.7850342988967896},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7443508505821228},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.6041104197502136},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5805267095565796},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.541658878326416},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.5332115888595581},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5096864700317383},{"id":"https://openalex.org/keywords/random-projection","display_name":"Random projection","score":0.4867711067199707},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.47525712847709656},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.4487454295158386},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4348582923412323},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.41789400577545166},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.36207884550094604},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34687310457229614},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.30965232849121094},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.2690359354019165},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11935418844223022},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.11358925700187683}],"concepts":[{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.7850342988967896},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7443508505821228},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.6041104197502136},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5805267095565796},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.541658878326416},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.5332115888595581},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5096864700317383},{"id":"https://openalex.org/C2777036070","wikidata":"https://www.wikidata.org/wiki/Q18393452","display_name":"Random projection","level":2,"score":0.4867711067199707},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.47525712847709656},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.4487454295158386},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4348582923412323},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.41789400577545166},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.36207884550094604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34687310457229614},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30965232849121094},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.2690359354019165},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11935418844223022},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.11358925700187683},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/globecom46510.2021.9685320","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom46510.2021.9685320","pdf_url":null,"source":{"id":"https://openalex.org/S4363607714","display_name":"2021 IEEE Global Communications Conference (GLOBECOM)","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":"2021 IEEE Global Communications Conference (GLOBECOM)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/3f01e936-8433-4f62-b8cb-f5d189bc35fd","is_oa":false,"landing_page_url":"https://research.manchester.ac.uk/en/publications/3f01e936-8433-4f62-b8cb-f5d189bc35fd","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sonee, A, Rini, S & Huang, Y-C 2022, Wireless Federated Learning with Limited Communication and Differential Privacy. in 2021 IEEE Global Communications Conference (GLOBECOM). IEEE, Piscataway, NJ, pp. 1-6, 2021 IEEE Global Communications Conference, Madrid, Spain, 7/12/21. https://doi.org/10.1109/globecom46510.2021.9685320","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals","score":0.5}],"awards":[{"id":"https://openalex.org/G6166732772","display_name":null,"funder_award_id":"MOST 110-2636-E-009-016","funder_id":"https://openalex.org/F4320309618","funder_display_name":"Ministry of Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320309618","display_name":"Ministry of Science and Technology","ror":"https://ror.org/02b207r52"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2027595342","https://openalex.org/W2112269233","https://openalex.org/W2163907882","https://openalex.org/W2535690855","https://openalex.org/W2804268522","https://openalex.org/W2963189173","https://openalex.org/W2963766684","https://openalex.org/W2964267428","https://openalex.org/W2979473749","https://openalex.org/W2981138228","https://openalex.org/W3006919779","https://openalex.org/W3013318908","https://openalex.org/W3016632787","https://openalex.org/W3027827208","https://openalex.org/W3081130510","https://openalex.org/W3091476023","https://openalex.org/W3101036738","https://openalex.org/W3111192549","https://openalex.org/W3135434750","https://openalex.org/W3158434734","https://openalex.org/W3169848021","https://openalex.org/W4205228770","https://openalex.org/W4288022516","https://openalex.org/W4299583294","https://openalex.org/W6751754709","https://openalex.org/W6769341872","https://openalex.org/W6785359441"],"related_works":["https://openalex.org/W2790862734","https://openalex.org/W2515532094","https://openalex.org/W345943785","https://openalex.org/W2141406155","https://openalex.org/W3015962327","https://openalex.org/W2611813480","https://openalex.org/W2742699808","https://openalex.org/W2119226345","https://openalex.org/W2624745934","https://openalex.org/W2807562011"],"abstract_inverted_index":{"This":[0,166],"paper":[1],"investigates":[2],"the":[3,16,20,35,72,77,98,107,117,120,139,159,162,169,177,183,187,190,208],"role":[4],"of":[5,15,76,100,119,151,161],"dimensionality":[6,180],"reduction":[7],"in":[8,38,203],"efficient":[9],"communication":[10,214],"and":[11,52,123,194],"differential":[12],"privacy":[13,193],"(DP)":[14],"local":[17,108,121,140],"datasets":[18],"at":[19,71],"remote":[21],"users":[22],"for":[23,96,115,186],"over-the-air":[24],"computation":[25],"(AirComp)-based":[26],"federated":[27,91],"learning":[28,47],"(FL)":[29],"model.":[30],"More":[31],"precisely,":[32],"we":[33],"consider":[34],"FL":[36],"setting":[37],"which":[39],"clients":[40],"are":[41],"prompted":[42],"to":[43,127,148,176,201],"train":[44],"a":[45,56,61,101],"machine":[46],"model":[48],"by":[49],"simultaneous":[50],"channel-aware":[51],"limited":[53],"communications":[54],"with":[55,211],"parameter":[57],"server":[58],"(PS)":[59],"over":[60],"Gaussian":[62],"multiple-access":[63],"channel":[64,78],"(GMAC),":[65],"so":[66],"that":[67,138],"transmissions":[68],"sum":[69],"coherently":[70],"PS":[73],"globally":[74],"aware":[75],"coefficients.":[79],"For":[80,132],"this":[81,133],"setting,":[82],"an":[83],"algorithm":[84],"is":[85,144,167,172,196,199],"proposed":[86],"based":[87,105],"on":[88,106,154],"applying":[89],"(i)":[90],"stochastic":[92],"gradient":[93],"descent":[94],"(FedSGD)":[95],"training":[97],"minimum":[99],"given":[102],"loss":[103],"function":[104],"gradients,":[109],"(ii)":[110],"Johnson-Lindenstrauss":[111],"(JL)":[112],"random":[113],"projection":[114],"reducing":[116],"dimension":[118,156],"updates":[122],"(iii)":[124],"artificial":[125],"noise":[126,150],"further":[128],"aid":[129],"user&#x0027;s":[130],"privacy.":[131],"scheme,":[134],"our":[135],"results":[136],"show":[137],"DP":[141],"(LDP)":[142],"performance":[143,184],"mainly":[145],"improved":[146],"due":[147],"injecting":[149],"greater":[152],"variance":[153],"each":[155],"while":[157,168],"keeping":[158],"sensitivity":[160],"projected":[163],"vectors":[164],"unchanged.":[165],"convergence":[170,195],"rate":[171],"slowed":[173],"down":[174],"compared":[175],"case":[178],"without":[179],"reduction.":[181],"As":[182],"outweighs":[185],"slower":[188],"convergence,":[189],"trade-off":[191,210],"between":[192],"higher":[197],"but":[198],"shown":[200],"lessen":[202],"high-dimensional":[204],"regime":[205],"yielding":[206],"almost":[207],"same":[209],"much":[212],"less":[213],"cost.":[215]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
