{"id":"https://openalex.org/W3089892974","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206841","title":"A Light-Weight Crowdsourcing Aggregation in Privacy-Preserving Federated Learning System","display_name":"A Light-Weight Crowdsourcing Aggregation in Privacy-Preserving Federated Learning System","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3089892974","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206841","mag":"3089892974"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206841","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206841","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5100365686","display_name":"Ke Zhang","orcid":"https://orcid.org/0000-0001-7201-5859"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Ke Zhang","raw_affiliation_strings":["The University of Hong Kong, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025130883","display_name":"Siu Ming Yiu","orcid":"https://orcid.org/0000-0002-3975-8500"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Siu Ming Yiu","raw_affiliation_strings":["The University of Hong Kong, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109853326","display_name":"Lucas C. K. Hui","orcid":null},"institutions":[{"id":"https://openalex.org/I146617529","display_name":"Applied Science and Technology Research Institute","ror":"https://ror.org/03xmkea05","country_code":"CN","type":"facility","lineage":["https://openalex.org/I146617529"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lucas Chi Kwong Hui","raw_affiliation_strings":["Hong Kong Applied Science and Technology Research Institute, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hong Kong Applied Science and Technology Research Institute, Hong Kong, China","institution_ids":["https://openalex.org/I146617529"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.575,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.69911463,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.995199978351593,"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/T10237","display_name":"Cryptography and Data Security","score":0.9876000285148621,"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":0.8720803260803223},{"id":"https://openalex.org/keywords/crowdsourcing","display_name":"Crowdsourcing","score":0.7722465991973877},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.7473025321960449},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6485756635665894},{"id":"https://openalex.org/keywords/majority-rule","display_name":"Majority rule","score":0.6398804187774658},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6269745826721191},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5847777128219604},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4894026815891266},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.480256587266922},{"id":"https://openalex.org/keywords/voting","display_name":"Voting","score":0.4775741398334503},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.45344075560569763},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.45317158102989197},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4472193419933319},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4283289313316345},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.41250404715538025},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.12192636728286743}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8720803260803223},{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.7722465991973877},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7473025321960449},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6485756635665894},{"id":"https://openalex.org/C153668964","wikidata":"https://www.wikidata.org/wiki/Q27636","display_name":"Majority rule","level":2,"score":0.6398804187774658},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6269745826721191},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5847777128219604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4894026815891266},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.480256587266922},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.4775741398334503},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.45344075560569763},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.45317158102989197},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4472193419933319},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4283289313316345},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.41250404715538025},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.12192636728286743},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9206841","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206841","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1529026127","https://openalex.org/W2045114813","https://openalex.org/W2119830539","https://openalex.org/W2140890285","https://openalex.org/W2165372647","https://openalex.org/W2283463896","https://openalex.org/W2530417694","https://openalex.org/W2535690855","https://openalex.org/W2535838896","https://openalex.org/W2807006176","https://openalex.org/W2808423829","https://openalex.org/W2810065831","https://openalex.org/W2912213068","https://openalex.org/W2914583895","https://openalex.org/W2930926105","https://openalex.org/W2949777041","https://openalex.org/W2970408908","https://openalex.org/W3102330763","https://openalex.org/W3103245149","https://openalex.org/W4297687186","https://openalex.org/W6677811664","https://openalex.org/W6680834967","https://openalex.org/W6684615325","https://openalex.org/W6695838908","https://openalex.org/W6728484576","https://openalex.org/W6752029299","https://openalex.org/W6752600739","https://openalex.org/W6764838729"],"related_works":["https://openalex.org/W2039876276","https://openalex.org/W2605569989","https://openalex.org/W2973321216","https://openalex.org/W4243868241","https://openalex.org/W1976415306","https://openalex.org/W2915435096","https://openalex.org/W2121434426","https://openalex.org/W2992989722","https://openalex.org/W3080832531","https://openalex.org/W2905430408"],"abstract_inverted_index":{"Federated":[0,162],"Machine":[1],"Learning":[2,163],"(FML)":[3],"sheds":[4],"light":[5],"on":[6,128],"secure":[7],"distributed":[8,85],"machine":[9],"learning.":[10],"However,":[11],"generic":[12],"FML":[13],"methods":[14],"may":[15],"lead":[16],"to":[17,77,144],"privacy-leakage":[18],"through":[19],"the":[20,34,59,91,135,205,210,215,226,236],"sharing":[21],"of":[22,25,55,89,94,104,132,189,214],"training":[23,35,61,92],"information":[24,93],"individual":[26,38,95],"models":[27,39,50,83,96],"and":[28,42,66,106,209,243],"have":[29],"relatively":[30,101],"poor":[31],"performance":[32],"when":[33],"datasets":[36,62],"for":[37,147,179,240],"are":[40,63],"biased":[41],"diversified.":[43],"This":[44],"is":[45],"a":[46,74,100,113,129,138,148,154,175],"problem":[47],"in":[48,52,84,199,217],"combining":[49],"trained":[51],"different":[53,82,120],"scenarios":[54],"IoT":[56,228],"devices":[57],"since":[58],"available":[60],"usually":[64],"limited":[65,130],"biased.":[67],"To":[68],"tackle":[69],"this":[70],"problem,":[71],"we":[72,110],"propose":[73],"novel":[75],"approach":[76],"precisely":[78],"ensemble":[79,216],"results":[80,122,146],"from":[81,123],"edge":[86,124],"devices.":[87,125],"Instead":[88],"passing":[90],"around":[97],"that":[98,117,198,223],"requires":[99],"large":[102],"amount":[103,131],"bandwidth":[105],"compromises":[107],"data":[108],"privacy,":[109],"suggest":[111],"employing":[112],"trusted":[114],"central":[115],"agent":[116,136],"only":[118],"collects":[119],"inference":[121],"Then":[126],"based":[127],"labeled":[133],"data,":[134],"runs":[137],"designed":[139],"statistical":[140],"iterative":[141],"crowdsourcing":[142],"algorithm":[143],"combine":[145],"more":[149,176],"accurate":[150,177],"aggregated":[151],"prediction":[152,178],"towards":[153],"user":[155],"query.":[156],"Our":[157],"proposed":[158],"system":[159],"model,":[160],"\"Privacy-Preserving":[161],"System\",":[164],"together":[165],"with":[166],"our":[167,200],"light-weight":[168],"Secure":[169],"Crowdsourcing":[170],"Aggregation":[171],"(SC-Agg)":[172],"algorithm,":[173],"provide":[174],"outside":[180],"queries":[181],"at":[182],"little":[183],"cost":[184],"without":[185],"any":[186],"prior":[187],"knowledge":[188],"what":[190],"query":[191],"will":[192],"be":[193],"submitted.":[194],"We":[195,221],"experimentally":[196],"verify":[197],"system,":[201],"SC-Agg":[202,224],"consistently":[203],"outperforms":[204],"majority":[206,238],"voting":[207],"method":[208],"best":[211],"performing":[212],"model":[213],"all":[218],"testing":[219],"scenarios.":[220],"believe":[222],"fits":[225],"real-world":[227],"applications":[229],"better":[230,244],"than":[231],"other":[232],"methods,":[233],"such":[234],"as":[235],"vanilla":[237],"voting,":[239],"its":[241],"robustness":[242],"performance.":[245]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}