{"id":"https://openalex.org/W4390098340","doi":"https://doi.org/10.23919/wiopt58741.2023.10349854","title":"Tackling Privacy Heterogeneity in Federated Learning","display_name":"Tackling Privacy Heterogeneity in Federated Learning","publication_year":2023,"publication_date":"2023-08-24","ids":{"openalex":"https://openalex.org/W4390098340","doi":"https://doi.org/10.23919/wiopt58741.2023.10349854"},"language":"en","primary_location":{"id":"doi:10.23919/wiopt58741.2023.10349854","is_oa":false,"landing_page_url":"http://dx.doi.org/10.23919/wiopt58741.2023.10349854","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)","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/A5101020101","display_name":"Ruichen Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Ruichen Xu","raw_affiliation_strings":["The Chinese University of Hong Kong,Department of Information Engineering,Hong Kong","Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong,Department of Information Engineering,Hong Kong","institution_ids":["https://openalex.org/I177725633"]},{"raw_affiliation_string":"Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004874287","display_name":"Ying\u2013Jun Angela Zhang","orcid":"https://orcid.org/0000-0002-7304-6849"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Ying-Jun Angela Zhang","raw_affiliation_strings":["The Chinese University of Hong Kong,Department of Information Engineering,Hong Kong","Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong,Department of Information Engineering,Hong Kong","institution_ids":["https://openalex.org/I177725633"]},{"raw_affiliation_string":"Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062346297","display_name":"Jianwei Huang","orcid":"https://orcid.org/0000-0001-6631-1096"},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianwei Huang","raw_affiliation_strings":["School of Science and Engineering, Shenzhen Institute of Artificial Intelligence and Robotics for Society, The Chinese University of Hong Kong, Shenzhen,Shenzhen,China,518172"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Science and Engineering, Shenzhen Institute of Artificial Intelligence and Robotics for Society, The Chinese University of Hong Kong, Shenzhen,Shenzhen,China,518172","institution_ids":["https://openalex.org/I4210116924"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4356,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.6260606,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"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":0.9998999834060669,"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":0.9998999834060669,"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":0.9890999794006348,"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":0.8008564710617065},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.73317950963974},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5936164259910583},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.5706453919410706},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4851113557815552},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.4570389986038208},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4562548100948334},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4383341073989868},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4163901209831238},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.41611480712890625},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.3632867932319641},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.31883013248443604}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8008564710617065},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.73317950963974},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5936164259910583},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.5706453919410706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4851113557815552},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.4570389986038208},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4562548100948334},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4383341073989868},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4163901209831238},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.41611480712890625},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3632867932319641},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31883013248443604},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/wiopt58741.2023.10349854","is_oa":false,"landing_page_url":"http://dx.doi.org/10.23919/wiopt58741.2023.10349854","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)","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":24,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2473418344","https://openalex.org/W2810715221","https://openalex.org/W2963062476","https://openalex.org/W2970408908","https://openalex.org/W3092041434","https://openalex.org/W3174531954","https://openalex.org/W3213145617","https://openalex.org/W4205228770","https://openalex.org/W4283022368","https://openalex.org/W4288346602","https://openalex.org/W4318619660","https://openalex.org/W6728757088","https://openalex.org/W6747855403","https://openalex.org/W6752985224","https://openalex.org/W6763192249","https://openalex.org/W6764838729","https://openalex.org/W6765541894","https://openalex.org/W6773817997","https://openalex.org/W6783635763","https://openalex.org/W6784790197","https://openalex.org/W6795487366","https://openalex.org/W6797161312","https://openalex.org/W6838846876"],"related_works":["https://openalex.org/W2950475743","https://openalex.org/W4386603768","https://openalex.org/W2886711096","https://openalex.org/W4380078352","https://openalex.org/W3036048022","https://openalex.org/W4309224979","https://openalex.org/W3026879719","https://openalex.org/W3196405711","https://openalex.org/W3013696689","https://openalex.org/W3187232590"],"abstract_inverted_index":{"Differentially":[0],"private":[1],"federated":[2,105],"learning":[3,150],"enables":[4],"clients":[5],"with":[6,155],"privacy":[7,25,42,56,80,96],"concerns":[8],"to":[9,31,128,169],"collaboratively":[10],"train":[11],"a":[12,100,117,134],"model":[13],"while":[14],"preserving":[15],"their":[16,29],"privacy.":[17],"Clients'":[18],"locally":[19],"available":[20],"data":[21],"and":[22,44,95,124],"maximum":[23],"tolerable":[24],"budgets":[26,43,57],"will":[27],"affect":[28],"contributions":[30],"the":[32,51,63,77,112,142,158,164],"training":[33,83,102,114],"performance.":[34,151],"To":[35],"date,":[36],"existing":[37],"studies":[38,49],"focus":[39],"on":[40,82,171],"homogeneous":[41],"thus":[45,125],"there":[46],"lack":[47],"systematic":[48],"regarding":[50],"impact":[52],"of":[53,79,120],"clients'":[54],"diverse":[55],"(privacy":[58],"heterogeneity).":[59],"This":[60],"paper":[61],"represents":[62],"first":[64],"step":[65],"toward":[66],"filling":[67],"this":[68],"gap.":[69],"Through":[70],"rigorous":[71],"convergence":[72],"analysis,":[73],"we":[74],"illustrate":[75],"that":[76,111,141],"influence":[78],"protection":[81,97],"loss":[84,166],"is":[85,116],"affected":[86],"by":[87,167],"client":[88,93,121,131,144,160],"selection":[89,94,122,132,145,161],"probabilities.":[90],"In":[91],"addition,":[92],"together":[98],"induce":[99],"non-vanishing":[101,113],"error":[103,115],"in":[104],"learning.":[106],"Our":[107],"analysis":[108],"then":[109],"shows":[110],"convex":[118,135],"function":[119],"probabilities":[123],"allows":[126],"us":[127],"formulate":[129],"privacy-aware":[130,143,159],"as":[133],"optimization":[136],"problem.":[137],"Numerical":[138],"results":[139],"demonstrate":[140],"strategy":[146,162],"can":[147],"significantly":[148],"improve":[149],"For":[152],"example,":[153],"compared":[154],"unbiased":[156],"selection,":[157],"decreases":[163],"test":[165],"up":[168],"63%":[170],"MNIST":[172],"convolutional":[173],"neural":[174],"network":[175],"classifier.":[176]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}