{"id":"https://openalex.org/W4402352308","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651264","title":"QI-DPFL: Quality-Aware and Incentive-Boosted Federated Learning with Differential Privacy","display_name":"QI-DPFL: Quality-Aware and Incentive-Boosted Federated Learning with Differential Privacy","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402352308","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651264"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10651264","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10651264","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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/A5101929201","display_name":"Wenhao Yuan","orcid":"https://orcid.org/0000-0003-0020-7629"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenhao Yuan","raw_affiliation_strings":["Sun Yat-Sen University,School of Artificial Intelligence,Zhuhai,China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University,School of Artificial Intelligence,Zhuhai,China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078667038","display_name":"Xuehe Wang","orcid":"https://orcid.org/0000-0002-6910-468X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuehe Wang","raw_affiliation_strings":["Sun Yat-Sen University,School of Artificial Intelligence,Zhuhai,China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University,School of Artificial Intelligence,Zhuhai,China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101929201"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":1.0878,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.8126309,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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/T10237","display_name":"Cryptography and Data Security","score":0.9969000220298767,"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":0.9869999885559082,"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/differential-privacy","display_name":"Differential privacy","score":0.8992648124694824},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7438674569129944},{"id":"https://openalex.org/keywords/incentive","display_name":"Incentive","score":0.6375157833099365},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.6128652691841125},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5575133562088013},{"id":"https://openalex.org/keywords/privacy-protection","display_name":"Privacy protection","score":0.4387875497341156},{"id":"https://openalex.org/keywords/internet-privacy","display_name":"Internet privacy","score":0.4194231629371643},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.41518110036849976},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2395758330821991},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.19417569041252136}],"concepts":[{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.8992648124694824},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7438674569129944},{"id":"https://openalex.org/C29122968","wikidata":"https://www.wikidata.org/wiki/Q1414816","display_name":"Incentive","level":2,"score":0.6375157833099365},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6128652691841125},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5575133562088013},{"id":"https://openalex.org/C3017597292","wikidata":"https://www.wikidata.org/wiki/Q25052250","display_name":"Privacy protection","level":2,"score":0.4387875497341156},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.4194231629371643},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.41518110036849976},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2395758330821991},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19417569041252136},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10651264","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10651264","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1987190000","https://openalex.org/W2112269233","https://openalex.org/W2112796928","https://openalex.org/W2573064842","https://openalex.org/W2594311007","https://openalex.org/W2734358244","https://openalex.org/W2963699739","https://openalex.org/W2970408908","https://openalex.org/W2972570881","https://openalex.org/W2978422189","https://openalex.org/W3025029677","https://openalex.org/W3112877893","https://openalex.org/W3112984084","https://openalex.org/W3118608800","https://openalex.org/W3120176213","https://openalex.org/W3122462552","https://openalex.org/W3170790803","https://openalex.org/W3200400919","https://openalex.org/W3201798506","https://openalex.org/W4205228770","https://openalex.org/W4283220764","https://openalex.org/W4285280274","https://openalex.org/W4317038464","https://openalex.org/W4318619660","https://openalex.org/W4321608239","https://openalex.org/W4382239708","https://openalex.org/W4382935786","https://openalex.org/W6676963778","https://openalex.org/W6728757088","https://openalex.org/W6731797260","https://openalex.org/W6764838729","https://openalex.org/W6767676916","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W4286971788","https://openalex.org/W3199340467","https://openalex.org/W3157608626","https://openalex.org/W3132132958","https://openalex.org/W4321612632","https://openalex.org/W4322580403","https://openalex.org/W3193217249","https://openalex.org/W4280591108","https://openalex.org/W3021849752","https://openalex.org/W3010781909"],"abstract_inverted_index":{"Federated":[0,75],"Learning":[1,76],"(FL)":[2],"has":[3],"increasingly":[4],"been":[5],"recognized":[6],"as":[7,131],"an":[8,116],"innovative":[9],"and":[10,73,127,155,159,212],"secure":[11],"distributed":[12],"model":[13,27,45],"training":[14,46],"paradigm,":[15],"aiming":[16],"to":[17,22,40,80,109,144,167,180],"coordinate":[18],"multiple":[19],"edge":[20,38],"clients":[21,88,130],"collaboratively":[23],"train":[24],"a":[25,67,95,132],"shared":[26],"without":[28],"uploading":[29],"their":[30],"private":[31],"datasets.":[32],"The":[33,171,189],"challenge":[34],"of":[35,174,200,209],"encouraging":[36],"mobile":[37],"devices":[39],"participate":[41],"zealously":[42],"in":[43,185],"FL":[44,203],"procedures,":[47],"while":[48],"mitigating":[49],"the":[50,82,102,121,124,128,137,141,150,164,175,182,198,207],"privacy":[51,165,210],"leakage":[52],"risks":[53],"during":[54],"wireless":[55],"transmission,":[56],"remains":[57],"comparatively":[58],"unexplored":[59],"so":[60],"far.":[61],"In":[62],"this":[63],"paper,":[64],"we":[65,92,114],"propose":[66,94],"novel":[68],"approach,":[69],"named":[70],"QI-DPFL":[71],"(Quality-Aware":[72],"Incentive-Boosted":[74],"with":[77,89],"Differential":[78],"Privacy),":[79],"address":[81],"aforementioned":[83],"intractable":[84],"issue.":[85],"To":[86],"select":[87],"high-quality":[90,111],"datasets,":[91],"first":[93],"quality-aware":[96],"client":[97,162],"selection":[98],"mechanism":[99,118],"based":[100],"on":[101,193],"Earth":[103],"Mover\u2019s":[104],"Distance":[105],"(EMD)":[106],"metric.":[107],"Furthermore,":[108],"attract":[110],"data":[112],"contributors,":[113],"design":[115],"incentive-boosted":[117],"that":[119],"constructs":[120],"interactions":[122],"between":[123,152],"central":[125,138],"server":[126,139],"selected":[129,161],"two-stage":[133],"Stackelberg":[134,176],"game,":[135],"where":[136],"designs":[140],"time-dependent":[142],"reward":[143,157],"minimize":[145],"its":[146,169],"cost":[147],"by":[148,205],"considering":[149],"trade-off":[151],"accuracy":[153],"loss":[154],"total":[156],"allocated,":[158],"each":[160,186],"decides":[163],"budget":[166],"maximize":[168],"utility.":[170],"Nash":[172],"Equilibrium":[173],"game":[177],"is":[178],"derived":[179],"find":[181],"optimal":[183],"solution":[184],"global":[187],"iteration.":[188],"extensive":[190],"experimental":[191],"results":[192],"different":[194],"real-world":[195],"datasets":[196],"demonstrate":[197],"effectiveness":[199],"our":[201],"proposed":[202],"framework,":[204],"realizing":[206],"goal":[208],"protection":[211],"incentive":[213],"compatibility.":[214]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
