{"id":"https://openalex.org/W4415125444","doi":"https://doi.org/10.1109/icmlt65785.2025.11193238","title":"Federated Learning With Individualized Privacy Through Client Sampling","display_name":"Federated Learning With Individualized Privacy Through Client Sampling","publication_year":2025,"publication_date":"2025-05-23","ids":{"openalex":"https://openalex.org/W4415125444","doi":"https://doi.org/10.1109/icmlt65785.2025.11193238"},"language":"en","primary_location":{"id":"doi:10.1109/icmlt65785.2025.11193238","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlt65785.2025.11193238","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 10th International Conference on Machine Learning Technologies (ICMLT)","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/A5101422183","display_name":"Lucas Lange","orcid":"https://orcid.org/0000-0002-6745-0845"},"institutions":[{"id":"https://openalex.org/I147765834","display_name":"Leipzig University of Applied Sciences","ror":"https://ror.org/03xgcq477","country_code":"DE","type":"education","lineage":["https://openalex.org/I147765834"]},{"id":"https://openalex.org/I4401726909","display_name":"Center for Scalable Data Analytics and Artificial Intelligence","ror":"https://ror.org/01t4ttr56","country_code":"DE","type":"education","lineage":["https://openalex.org/I4401726909","https://openalex.org/I78650965","https://openalex.org/I926574661"]},{"id":"https://openalex.org/I926574661","display_name":"Leipzig University","ror":"https://ror.org/03s7gtk40","country_code":"DE","type":"education","lineage":["https://openalex.org/I926574661"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lucas Lange","raw_affiliation_strings":["Leipzig University,ScaDS.AI Dresden/Leipzig,Leipzig,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Leipzig University,ScaDS.AI Dresden/Leipzig,Leipzig,Germany","institution_ids":["https://openalex.org/I147765834","https://openalex.org/I4401726909","https://openalex.org/I926574661"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072421078","display_name":"Ole Borchardt","orcid":null},"institutions":[{"id":"https://openalex.org/I147765834","display_name":"Leipzig University of Applied Sciences","ror":"https://ror.org/03xgcq477","country_code":"DE","type":"education","lineage":["https://openalex.org/I147765834"]},{"id":"https://openalex.org/I4401726909","display_name":"Center for Scalable Data Analytics and Artificial Intelligence","ror":"https://ror.org/01t4ttr56","country_code":"DE","type":"education","lineage":["https://openalex.org/I4401726909","https://openalex.org/I78650965","https://openalex.org/I926574661"]},{"id":"https://openalex.org/I926574661","display_name":"Leipzig University","ror":"https://ror.org/03s7gtk40","country_code":"DE","type":"education","lineage":["https://openalex.org/I926574661"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ole Borchardt","raw_affiliation_strings":["Leipzig University,ScaDS.AI Dresden/Leipzig,Leipzig,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Leipzig University,ScaDS.AI Dresden/Leipzig,Leipzig,Germany","institution_ids":["https://openalex.org/I147765834","https://openalex.org/I4401726909","https://openalex.org/I926574661"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075756237","display_name":"Erhard Rahm","orcid":"https://orcid.org/0000-0002-2665-1114"},"institutions":[{"id":"https://openalex.org/I147765834","display_name":"Leipzig University of Applied Sciences","ror":"https://ror.org/03xgcq477","country_code":"DE","type":"education","lineage":["https://openalex.org/I147765834"]},{"id":"https://openalex.org/I4401726909","display_name":"Center for Scalable Data Analytics and Artificial Intelligence","ror":"https://ror.org/01t4ttr56","country_code":"DE","type":"education","lineage":["https://openalex.org/I4401726909","https://openalex.org/I78650965","https://openalex.org/I926574661"]},{"id":"https://openalex.org/I926574661","display_name":"Leipzig University","ror":"https://ror.org/03s7gtk40","country_code":"DE","type":"education","lineage":["https://openalex.org/I926574661"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Erhard Rahm","raw_affiliation_strings":["Leipzig University,ScaDS.AI Dresden/Leipzig,Leipzig,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Leipzig University,ScaDS.AI Dresden/Leipzig,Leipzig,Germany","institution_ids":["https://openalex.org/I147765834","https://openalex.org/I4401726909","https://openalex.org/I926574661"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.673,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.85945213,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"213","last_page":"217"},"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/T11045","display_name":"Privacy, Security, and Data Protection","score":0.968999981880188,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9362000226974487,"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/differential-privacy","display_name":"Differential privacy","score":0.8199999928474426},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.6370000243186951},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.5759999752044678},{"id":"https://openalex.org/keywords/privacy-software","display_name":"Privacy software","score":0.5491999983787537},{"id":"https://openalex.org/keywords/privacy-protection","display_name":"Privacy protection","score":0.5228000283241272},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5040000081062317},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5027999877929688},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4699999988079071}],"concepts":[{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.8199999928474426},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7856000065803528},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6370000243186951},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.5759999752044678},{"id":"https://openalex.org/C509729295","wikidata":"https://www.wikidata.org/wiki/Q7246032","display_name":"Privacy software","level":3,"score":0.5491999983787537},{"id":"https://openalex.org/C3017597292","wikidata":"https://www.wikidata.org/wiki/Q25052250","display_name":"Privacy protection","level":2,"score":0.5228000283241272},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5040000081062317},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5027999877929688},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4699999988079071},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.461899995803833},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3580999970436096},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.33149999380111694},{"id":"https://openalex.org/C169093310","wikidata":"https://www.wikidata.org/wiki/Q3702971","display_name":"Personally identifiable information","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C193934123","wikidata":"https://www.wikidata.org/wiki/Q7246028","display_name":"Privacy by Design","level":3,"score":0.31459999084472656},{"id":"https://openalex.org/C71745522","wikidata":"https://www.wikidata.org/wiki/Q2476929","display_name":"Confidentiality","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C2910417920","wikidata":"https://www.wikidata.org/wiki/Q4116434","display_name":"Patient privacy","level":3,"score":0.2745000123977661},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C47487241","wikidata":"https://www.wikidata.org/wiki/Q5227230","display_name":"Data access","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.25870001316070557},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.25760000944137573},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlt65785.2025.11193238","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlt65785.2025.11193238","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 10th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320311649","display_name":"Ministry of Education","ror":"https://ror.org/036nq5137"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2001610032","https://openalex.org/W2002787959","https://openalex.org/W2072295764","https://openalex.org/W2097427281","https://openalex.org/W2106490775","https://openalex.org/W2113792377","https://openalex.org/W2473418344","https://openalex.org/W2795435272","https://openalex.org/W2911978475","https://openalex.org/W2963672937","https://openalex.org/W2995022099","https://openalex.org/W3016632787","https://openalex.org/W3135347465","https://openalex.org/W3173181676","https://openalex.org/W3192324887","https://openalex.org/W3198421477","https://openalex.org/W4225821859","https://openalex.org/W4324066858","https://openalex.org/W4385187849","https://openalex.org/W4385412495","https://openalex.org/W4392353733","https://openalex.org/W4413175474"],"related_works":[],"abstract_inverted_index":{"With":[0],"growing":[1],"concerns":[2],"about":[3],"user":[4,24],"data":[5],"collection,":[6],"individualized":[7],"privacy":[8,25,44,78,99,115,138],"has":[9],"emerged":[10],"as":[11],"a":[12,30,105],"promising":[13],"solution":[14],"to":[15,42,75,88,142,155],"balance":[16],"protection":[17],"and":[18,101,117,139],"utility":[19],"by":[20,71],"accounting":[21],"for":[22,35,61,165],"diverse":[23],"preferences.":[26,79],"Instead":[27],"of":[28,33,176],"enforcing":[29],"uniform":[31,131],"level":[32],"anonymization":[34],"all":[36],"users,":[37],"this":[38,54,111],"approach":[39,126],"allows":[40],"individuals":[41],"choose":[43],"settings":[45,87],"that":[46,124],"align":[47],"with":[48,168],"their":[49,76,97],"comfort":[50],"levels.":[51],"Building":[52],"on":[53,96],"idea,":[55],"we":[56,90],"propose":[57],"an":[58],"adapted":[59],"method":[60,112,146,158],"enabling":[62],"Individualized":[63],"Differential":[64],"Privacy":[65],"(IDP)":[66],"in":[67,147],"Federated":[68],"Learning":[69],"(FL)":[70],"handling":[72],"clients":[73],"according":[74],"personal":[77],"By":[80],"extending":[81],"the":[82,135,143,174,177],"SAMPLE":[83],"algorithm":[84],"from":[85,173],"centralized":[86],"FL,":[89],"calculate":[91],"client-specific":[92],"sampling":[93],"rates":[94],"based":[95],"heterogeneous":[98],"budgets":[100],"integrate":[102],"them":[103],"into":[104],"modified":[106],"IDP-FedAvg":[107],"algorithm.":[108],"We":[109],"test":[110],"under":[113],"realistic":[114],"distributions":[116],"multiple":[118],"datasets.":[119],"The":[120],"experimental":[121],"results":[122],"demonstrate":[123],"our":[125,157],"achieves":[127],"clear":[128],"improvements":[129],"over":[130],"DP":[132],"baselines,":[133],"reducing":[134],"trade-off":[136],"between":[137],"utility.":[140],"Compared":[141],"alternative":[144],"SCALE":[145],"related":[148],"work,":[149],"which":[150],"assigns":[151],"differing":[152],"noise":[153],"scales":[154],"clients,":[156],"performs":[159],"notably":[160],"better.":[161],"However,":[162],"challenges":[163],"remain":[164],"complex":[166],"tasks":[167],"non-i.i.d.":[169],"data,":[170],"primarily":[171],"stemming":[172],"constraints":[175],"decentralized":[178],"setting.":[179]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-14T00:00:00"}