{"id":"https://openalex.org/W4226183928","doi":"https://doi.org/10.1109/infocom48880.2022.9796935","title":"Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling","display_name":"Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling","publication_year":2022,"publication_date":"2022-05-02","ids":{"openalex":"https://openalex.org/W4226183928","doi":"https://doi.org/10.1109/infocom48880.2022.9796935"},"language":"en","primary_location":{"id":"doi:10.1109/infocom48880.2022.9796935","is_oa":false,"landing_page_url":"https://doi.org/10.1109/infocom48880.2022.9796935","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE INFOCOM 2022 - IEEE Conference on Computer Communications","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/A5010645658","display_name":"Bing Luo","orcid":"https://orcid.org/0000-0002-3664-5009"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]},{"id":"https://openalex.org/I4210104064","display_name":"Shenzhen Academy of Robotics","ror":"https://ror.org/01h027j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210104064"]},{"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","US"],"is_corresponding":false,"raw_author_name":"Bing Luo","raw_affiliation_strings":["Shenzhen Institute of Artificial Intelligence and Robotics for Society,China","Department of Electrical Engineering and Institute for Network Science, Yale University, USA","School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China","Shenzhen Institute of Artificial Intelligence and Robotics for Society, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Institute of Artificial Intelligence and Robotics for Society,China","institution_ids":["https://openalex.org/I4210104064"]},{"raw_affiliation_string":"Department of Electrical Engineering and Institute for Network Science, Yale University, USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]},{"raw_affiliation_string":"Shenzhen Institute of Artificial Intelligence and Robotics for Society, China","institution_ids":["https://openalex.org/I4210104064"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101341250","display_name":"Wenli Xiao","orcid":"https://orcid.org/0000-0001-7310-1652"},"institutions":[{"id":"https://openalex.org/I4210104064","display_name":"Shenzhen Academy of Robotics","ror":"https://ror.org/01h027j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210104064"]},{"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":"Wenli Xiao","raw_affiliation_strings":["School of Science and Engineering, The Chinese University of Hong Kong,Shenzhen,China","School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China","Shenzhen Institute of Artificial Intelligence and Robotics for Society, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Science and Engineering, The Chinese University of Hong Kong,Shenzhen,China","institution_ids":["https://openalex.org/I4210116924"]},{"raw_affiliation_string":"School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]},{"raw_affiliation_string":"Shenzhen Institute of Artificial Intelligence and Robotics for Society, China","institution_ids":["https://openalex.org/I4210104064"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100443968","display_name":"Shiqiang Wang","orcid":"https://orcid.org/0000-0003-2090-5512"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shiqiang Wang","raw_affiliation_strings":["IBM T. J. Watson Research Center,Yorktown Heights,NY,USA","IBM T. J. Watson Research Center, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center,Yorktown Heights,NY,USA","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM T. J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062346297","display_name":"Jianwei Huang","orcid":"https://orcid.org/0000-0001-6631-1096"},"institutions":[{"id":"https://openalex.org/I4210104064","display_name":"Shenzhen Academy of Robotics","ror":"https://ror.org/01h027j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210104064"]},{"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, The Chinese University of Hong Kong,Shenzhen,China","School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China","Shenzhen Institute of Artificial Intelligence and Robotics for Society, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Science and Engineering, The Chinese University of Hong Kong,Shenzhen,China","institution_ids":["https://openalex.org/I4210116924"]},{"raw_affiliation_string":"School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]},{"raw_affiliation_string":"Shenzhen Institute of Artificial Intelligence and Robotics for Society, China","institution_ids":["https://openalex.org/I4210104064"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014892027","display_name":"Leandros Tassiulas","orcid":"https://orcid.org/0000-0003-0932-774X"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leandros Tassiulas","raw_affiliation_strings":["Yale University,Department of Electrical Engineering and Institute for Network Science,USA","Department of Electrical Engineering and Institute for Network Science, Yale University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University,Department of Electrical Engineering and Institute for Network Science,USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Department of Electrical Engineering and Institute for Network Science, Yale University, USA","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":33.7322,"has_fulltext":false,"cited_by_count":214,"citation_normalized_percentile":{"value":0.99843466,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1739","last_page":"1748"},"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":0.9983999729156494,"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":0.9968000054359436,"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":0.7990440130233765},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4875916838645935},{"id":"https://openalex.org/keywords/statistical-learning","display_name":"Statistical learning","score":0.44933104515075684},{"id":"https://openalex.org/keywords/adaptive-sampling","display_name":"Adaptive sampling","score":0.42956864833831787},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38362979888916016},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3361041843891144},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14814910292625427},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.08847016096115112},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.079546719789505},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07330125570297241}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7990440130233765},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4875916838645935},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.44933104515075684},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.42956864833831787},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38362979888916016},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3361041843891144},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14814910292625427},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.08847016096115112},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.079546719789505},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07330125570297241},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/infocom48880.2022.9796935","is_oa":false,"landing_page_url":"https://doi.org/10.1109/infocom48880.2022.9796935","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE INFOCOM 2022 - IEEE Conference on Computer Communications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322170","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W1842094663","https://openalex.org/W2112796928","https://openalex.org/W2162287622","https://openalex.org/W2429473340","https://openalex.org/W2535838896","https://openalex.org/W2798720628","https://openalex.org/W2900182564","https://openalex.org/W2912213068","https://openalex.org/W2920095265","https://openalex.org/W2949934631","https://openalex.org/W2955213239","https://openalex.org/W2963179579","https://openalex.org/W2963318081","https://openalex.org/W2963490108","https://openalex.org/W2975156709","https://openalex.org/W2981206218","https://openalex.org/W2982654255","https://openalex.org/W3021654819","https://openalex.org/W3037047862","https://openalex.org/W3037871107","https://openalex.org/W3038022836","https://openalex.org/W3038028469","https://openalex.org/W3047304572","https://openalex.org/W3047595056","https://openalex.org/W3089655738","https://openalex.org/W3095276777","https://openalex.org/W3105122387","https://openalex.org/W3111182214","https://openalex.org/W3113075536","https://openalex.org/W3116579057","https://openalex.org/W3127965231","https://openalex.org/W3131195871","https://openalex.org/W3155160971","https://openalex.org/W3156310591","https://openalex.org/W3205478485","https://openalex.org/W3206985409","https://openalex.org/W3207233526","https://openalex.org/W4250589301","https://openalex.org/W4287647398","https://openalex.org/W4293363185","https://openalex.org/W4294106961","https://openalex.org/W4297511088","https://openalex.org/W4297687186","https://openalex.org/W4300092207","https://openalex.org/W4301183978","https://openalex.org/W4318619660","https://openalex.org/W6638704708","https://openalex.org/W6684012599","https://openalex.org/W6685644109","https://openalex.org/W6717867081","https://openalex.org/W6728757088","https://openalex.org/W6732298257","https://openalex.org/W6752191696","https://openalex.org/W6755251367","https://openalex.org/W6759226220","https://openalex.org/W6759238902","https://openalex.org/W6762930437","https://openalex.org/W6765541894","https://openalex.org/W6766019203","https://openalex.org/W6769579599","https://openalex.org/W6769624030","https://openalex.org/W6771536673","https://openalex.org/W6784048413","https://openalex.org/W6784343361","https://openalex.org/W6786507088","https://openalex.org/W6790212648","https://openalex.org/W6790579690"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W2038693912","https://openalex.org/W1991602789"],"abstract_inverted_index":{"Federated":[0],"learning":[1,116,138],"(FL)":[2],"algorithms":[3,98],"usually":[4],"sample":[5],"a":[6,91,124,148],"fraction":[7],"of":[8,18,35,60],"clients":[9],"in":[10,123,142,185],"each":[11],"round":[12],"(partial":[13],"participation)":[14],"when":[15],"the":[16,23,32,85,106,111,114,139,143,154,173,193,199],"number":[17],"participants":[19],"is":[20,27],"large":[21],"and":[22,63,80,118,146,163],"server\u2019s":[24],"communication":[25],"bandwidth":[26],"limited.":[28],"Recent":[29],"works":[30],"on":[31,39,105],"convergence":[33,55,87,94,144,174],"analysis":[34],"FL":[36,97],"have":[37],"focused":[38],"unbiased":[40],"client":[41,73,101],"sampling,":[42],"e.g.,":[43],"sampling":[44,74,102,119,169,180,195],"uniformly":[45],"at":[46],"random,":[47],"which":[48,121],"suffers":[49],"from":[50,159],"slow":[51],"wall-clock":[52,86],"time":[53,117,130,175,191],"for":[54,96,128,137,197],"due":[56],"to":[57,69,83,151,177],"high":[58],"degrees":[59],"system":[61,79],"heterogeneity":[62,82],"statistical":[64,81],"heterogeneity.":[65],"This":[66],"paper":[67],"aims":[68],"design":[70,133],"an":[71,134],"adaptive":[72],"algorithm":[75,136,150],"that":[76,166],"tackles":[77],"both":[78,160],"minimize":[84],"time.":[88],"We":[89,132],"obtain":[90],"new":[92],"tractable":[93],"bound":[95,145],"with":[99],"arbitrary":[100],"probabilities.":[103],"Based":[104],"bound,":[107],"we":[108],"analytically":[109],"establish":[110],"relationship":[112],"between":[113],"total":[115],"probabilities,":[120],"results":[122,158],"non-convex":[125,155],"optimization":[126],"problem":[127],"training":[129],"minimization.":[131],"efficient":[135],"unknown":[140],"parameters":[141],"develop":[147],"low-complexity":[149],"approximately":[152],"solve":[153],"problem.":[156],"Experimental":[157],"hardware":[161,186],"prototype":[162,187],"simulation":[164],"demonstrate":[165],"our":[167,183],"proposed":[168],"scheme":[170,184],"significantly":[171],"reduces":[172],"compared":[176],"several":[178],"baseline":[179,196],"schemes.":[181],"Notably,":[182],"spends":[188],"73%":[189],"less":[190],"than":[192],"uniform":[194],"reaching":[198],"same":[200],"target":[201],"loss.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":14},{"year":2025,"cited_by_count":70},{"year":2024,"cited_by_count":67},{"year":2023,"cited_by_count":54},{"year":2022,"cited_by_count":9}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}