{"id":"https://openalex.org/W3166300729","doi":null,"title":"Coded Federated Learning Framework for AI-Based Mobile Application Services with Privacy-Awareness.","display_name":"Coded Federated Learning Framework for AI-Based Mobile Application Services with Privacy-Awareness.","publication_year":2021,"publication_date":"2021-06-17","ids":{"openalex":"https://openalex.org/W3166300729","mag":"3166300729"},"language":"en","primary_location":{"id":"mag:3166300729","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2106.09261","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://export.arxiv.org/pdf/2106.09261","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031383490","display_name":"Yuris Mulya Saputra","orcid":"https://orcid.org/0000-0001-8862-2004","observed_orcids":["https://orcid.org/0000-0001-8862-2004"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuris Mulya Saputra","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-8862-2004","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100697893","display_name":"Diep Ngoc Nguyen","orcid":"https://orcid.org/0000-0003-2659-8648","observed_orcids":["https://orcid.org/0000-0003-2659-8648"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Diep N. Nguyen","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-2659-8648","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007992576","display_name":"Dinh Thai Hoang","orcid":"https://orcid.org/0000-0002-9528-0863","observed_orcids":["https://orcid.org/0000-0002-9528-0863"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dinh Thai Hoang","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-9528-0863","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5063537942","display_name":"Eryk Dutkiewicz","orcid":"https://orcid.org/0000-0002-4268-9286","observed_orcids":["https://orcid.org/0000-0002-4268-9286"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eryk Dutkiewicz","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-4268-9286","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":1,"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":0.9990000128746033,"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":0.9990000128746033,"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/T10270","display_name":"Blockchain Technology Applications and Security","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10273","display_name":"IoT and Edge/Fog Computing","score":9.999999747378752e-05,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mobile-application-services","display_name":"mobile application services","score":0.8500000238418579},{"id":"https://openalex.org/keywords/privacy-costs","display_name":"privacy costs","score":0.6850000023841858},{"id":"https://openalex.org/keywords/information-asymmetry","display_name":"information asymmetry","score":0.75},{"id":"https://openalex.org/keywords/social-welfare-welfare-economics","display_name":"social welfare (welfare economics)","score":0.6200000047683716},{"id":"https://openalex.org/keywords/user-privacy","display_name":"user privacy","score":0.3889999985694885},{"id":"https://openalex.org/keywords/communication-overhead","display_name":"communication overhead","score":0.4480000138282776}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8273262977600098},{"id":"https://openalex.org/C71901391","wikidata":"https://www.wikidata.org/wiki/Q7126699","display_name":"Upload","level":2,"score":0.609806478023529},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.4909127354621887},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.4855203628540039},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.46679550409317017},{"id":"https://openalex.org/C116537","wikidata":"https://www.wikidata.org/wiki/Q2169973","display_name":"Service provider","level":3,"score":0.4370713233947754},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.4186444580554962},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.41846275329589844},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4031803607940674},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.32493308186531067},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3236134648323059},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2850925922393799},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.15619924664497375},{"id":"https://openalex.org/C136264566","wikidata":"https://www.wikidata.org/wiki/Q159810","display_name":"Economy","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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3166300729","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2106.09261","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3166300729","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2106.09261","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W104209573","https://openalex.org/W1579157456","https://openalex.org/W1975471017","https://openalex.org/W1987442811","https://openalex.org/W1999417451","https://openalex.org/W2268029391","https://openalex.org/W2291868800","https://openalex.org/W2508718653","https://openalex.org/W2781091734","https://openalex.org/W2950863887","https://openalex.org/W2952051345","https://openalex.org/W2962804345","https://openalex.org/W2962883549","https://openalex.org/W2964121744","https://openalex.org/W2972882814","https://openalex.org/W2975802305","https://openalex.org/W2981556215","https://openalex.org/W2985145906","https://openalex.org/W3008490107","https://openalex.org/W3011267726","https://openalex.org/W3015636663","https://openalex.org/W3021221399","https://openalex.org/W3028867652","https://openalex.org/W3045747588","https://openalex.org/W3059999910","https://openalex.org/W3086286002","https://openalex.org/W3104691194","https://openalex.org/W3111819764","https://openalex.org/W3140753276","https://openalex.org/W3154864063","https://openalex.org/W3175100006"],"related_works":["https://openalex.org/W4210427479","https://openalex.org/W3208046223","https://openalex.org/W7036926933","https://openalex.org/W4396777661","https://openalex.org/W4405846658","https://openalex.org/W7027535041","https://openalex.org/W4391853594","https://openalex.org/W4282968198","https://openalex.org/W4389170275","https://openalex.org/W4221163233"],"abstract_inverted_index":{"By":[0],"encoding":[1],"computing":[2,5,189],"tasks,":[3],"coded":[4,85,139,176],"can":[6,123,147,226],"not":[7],"only":[8],"mitigate":[9],"straggling":[10],"problems":[11],"in":[12,74],"federated":[13],"learning":[14,161],"(FL),":[15],"but":[16],"also":[17],"preserve":[18],"privacy":[19,184,259],"of":[20,106,150,163,250,265],"sensitive":[21],"data":[22,76,136],"uploaded/contributed":[23],"by":[24,34],"participating":[25,156,252],"mobile":[26,36,91],"users":[27],"(MUs)":[28],"to":[29,52,77,94,127,131,214,232,239,255],"the":[30,61,67,78,100,107,111,128,143,151,155,164,182,186,195,202,258],"centralized":[31],"server,":[32],"owned":[33],"a":[35,83,89,104,125,170,219],"application":[37,92],"provider":[38],"(MAP).":[39],"However,":[40],"these":[41,96],"advantages":[42],"come":[43],"with":[44,218,263],"extra":[45],"coding":[46],"cost/complexity":[47],"and":[48,69,137,153,191,197,209,234],"communication":[49],"overhead":[50],"(referred":[51],"as":[53],"\\emph{privacy":[54],"cost})":[55],"that":[56,146,223],"must":[57],"be":[58],"considered":[59],"given":[60],"limited":[62,188],"computing/communications":[63],"resources":[64],"at":[65],"MUs/MAP,":[66],"rationality":[68],"incentive":[70],"competition":[71],"among":[72],"MUs":[73,109,157],"contributing":[75],"MAP.":[79],"This":[80],"article":[81],"proposes":[82],"novel":[84],"FL-based":[86,177],"framework":[87,225],"for":[88,110],"privacy-aware":[90],"service":[93],"address":[95],"challenges.":[97],"In":[98],"particular,":[99],"MAP":[101,129,152,196],"first":[102,168],"determines":[103],"set":[105],"best":[108],"FL":[112],"process":[113],"based":[114],"on":[115],"MUs'":[116,183],"provided":[117],"information/features.":[118],"Then,":[119,199],"each":[120],"selected":[121],"MU":[122],"propose":[124],"contract":[126],"according":[130],"its":[132],"expected":[133],"trainable":[134],"local":[135],"privacy-protected":[138],"data.":[140],"To":[141],"find":[142],"optimal":[144],"contracts":[145],"maximize":[148],"utilities":[149],"all":[154,251],"while":[158,242],"maintaining":[159],"high":[160],"quality":[162],"whole":[165],"system,":[166],"we":[167,200],"develop":[169,210],"multi-principal":[171],"one-agent":[172],"contract-based":[173],"problem":[174,203,208],"leveraging":[175],"multiple":[178],"utility":[179,249],"functions":[180],"under":[181,257],"cost,":[185],"MAP's":[187],"resource,":[190],"asymmetric":[192],"information":[193],"between":[194],"MUs.":[198],"transform":[201],"into":[204],"an":[205,211],"equivalent":[206],"low-complexity":[207],"iterative":[212],"algorithm":[213],"solve":[215],"it.":[216],"Experiments":[217],"real-world":[220],"dataset":[221],"show":[222],"our":[224],"speed":[227],"up":[228,231,238,254],"training":[229],"time":[230],"49%":[233],"improve":[235],"prediction":[236],"accuracy":[237],"4.6":[240],"times":[241],"enhancing":[243],"network's":[244],"social":[245],"welfare,":[246],"i.e.,":[247],"total":[248],"entities,":[253],"114%":[256],"cost":[260],"consideration":[261],"compared":[262],"those":[264],"baseline":[266],"methods.":[267]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2021-06-22T00:00:00"}
