{"id":"https://openalex.org/W3196325542","doi":"https://doi.org/10.1109/iwqos52092.2021.9521361","title":"Optimizing Federated Learning on Device Heterogeneity with A Sampling Strategy","display_name":"Optimizing Federated Learning on Device Heterogeneity with A Sampling Strategy","publication_year":2021,"publication_date":"2021-06-25","ids":{"openalex":"https://openalex.org/W3196325542","doi":"https://doi.org/10.1109/iwqos52092.2021.9521361","mag":"3196325542"},"language":"en","primary_location":{"id":"doi:10.1109/iwqos52092.2021.9521361","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwqos52092.2021.9521361","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS)","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/A5101190554","display_name":"Xiaohui Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaohui Xu","raw_affiliation_strings":["School of Computer Science, Central South University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031745233","display_name":"Sijing Duan","orcid":"https://orcid.org/0000-0003-3295-8822"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sijing Duan","raw_affiliation_strings":["School of Computer Science, Central South University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101961350","display_name":"Jinrui Zhang","orcid":"https://orcid.org/0000-0001-9035-3050"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinrui Zhang","raw_affiliation_strings":["School of Computer Science, Central South University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053686418","display_name":"Yunzhen Luo","orcid":"https://orcid.org/0000-0002-9359-3401"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunzhen Luo","raw_affiliation_strings":["School of Computer Science, Central South University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100705648","display_name":"Deyu Zhang","orcid":"https://orcid.org/0000-0002-5676-1285"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Deyu Zhang","raw_affiliation_strings":["School of Computer Science, Central South University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139660479"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"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":0.9943000078201294,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9896000027656555,"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/mnist-database","display_name":"MNIST database","score":0.9128895998001099},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8343843221664429},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5780497789382935},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5701377987861633},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.555576503276825},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.5475810170173645},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47265636920928955},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4488433599472046},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4412737190723419},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"Distributed learning","score":0.4190899431705475},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.41892069578170776},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.34148770570755005},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3300318121910095},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.23236408829689026},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.19258487224578857},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.1632063090801239}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.9128895998001099},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8343843221664429},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5780497789382935},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5701377987861633},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.555576503276825},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.5475810170173645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47265636920928955},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4488433599472046},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4412737190723419},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.4190899431705475},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.41892069578170776},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.34148770570755005},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3300318121910095},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.23236408829689026},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.19258487224578857},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.1632063090801239},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwqos52092.2021.9521361","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwqos52092.2021.9521361","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322843","display_name":"Natural Science Foundation of\u00a0Hunan Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W2174940656","https://openalex.org/W2177410802","https://openalex.org/W2535838896","https://openalex.org/W2541884796","https://openalex.org/W2774000609","https://openalex.org/W2807006176","https://openalex.org/W2887280559","https://openalex.org/W2900120080","https://openalex.org/W2904760378","https://openalex.org/W2912592113","https://openalex.org/W2921434559","https://openalex.org/W2962688627","https://openalex.org/W2963803379","https://openalex.org/W2963933682","https://openalex.org/W2981298997","https://openalex.org/W3008187686","https://openalex.org/W3010312571","https://openalex.org/W3012798438","https://openalex.org/W3015636663","https://openalex.org/W3034713518","https://openalex.org/W3035523105","https://openalex.org/W3036013485","https://openalex.org/W3037842861","https://openalex.org/W3043723611","https://openalex.org/W3080934299","https://openalex.org/W3104631511","https://openalex.org/W3111182214","https://openalex.org/W3122122969","https://openalex.org/W3191604281","https://openalex.org/W4285876308","https://openalex.org/W4288101682","https://openalex.org/W4294106961","https://openalex.org/W4294583774","https://openalex.org/W4294635920","https://openalex.org/W4297687186","https://openalex.org/W4299283926","https://openalex.org/W4300092207","https://openalex.org/W4318619660","https://openalex.org/W6685644109","https://openalex.org/W6685663092","https://openalex.org/W6728757088","https://openalex.org/W6746839373","https://openalex.org/W6749892895","https://openalex.org/W6752029299","https://openalex.org/W6754002923","https://openalex.org/W6755988804","https://openalex.org/W6757292943","https://openalex.org/W6758757267","https://openalex.org/W6760214840","https://openalex.org/W6768537144","https://openalex.org/W6769627709","https://openalex.org/W6771536673","https://openalex.org/W6773077378","https://openalex.org/W6773976177","https://openalex.org/W6774707171","https://openalex.org/W6775488050","https://openalex.org/W6779269186","https://openalex.org/W6779544620","https://openalex.org/W6780440235","https://openalex.org/W6781318954","https://openalex.org/W6787507181"],"related_works":["https://openalex.org/W3196405711","https://openalex.org/W3187232590","https://openalex.org/W4317941881","https://openalex.org/W2998530156","https://openalex.org/W4323521275","https://openalex.org/W3035996294","https://openalex.org/W2954034773","https://openalex.org/W3013510494","https://openalex.org/W4229067761","https://openalex.org/W3091296419"],"abstract_inverted_index":{"Federated":[0,158],"learning":[1,7],"(FL)":[2],"is":[3],"a":[4,21,56,88,98],"novel":[5],"machine":[6],"that":[8,35,127],"performs":[9],"distributed":[10],"training":[11],"locally":[12],"on":[13,59,78,143,148,152],"devices":[14],"and":[15,28,52,72,84,106,150],"aggregating":[16],"the":[17,29,48,60,79,108,113,128,144,157],"local":[18,50],"models":[19],"into":[20],"global":[22],"one.":[23],"The":[24],"limited":[25],"network":[26],"bandwidth":[27],"tremendous":[30],"amount":[31],"of":[32,62,75,81,130],"model":[33,82],"data":[34],"need":[36],"to":[37,91,101,111,141,156],"be":[38,137],"transported":[39],"bring":[40],"up":[41,104,140],"expensive":[42],"communication":[43,131],"cost.":[44],"Meanwhile,":[45],"heterogeneity":[46,77],"in":[47,123,134,154],"devices\u2019":[49],"datasets":[51],"computation":[53],"power":[54],"exerts":[55],"huge":[57],"influence":[58],"performance":[61,80],"FL.":[63],"To":[64],"address":[65],"these":[66],"issues,":[67],"we":[68,96,125],"provide":[69],"an":[70],"empirical":[71],"mathematical":[73],"analysis":[74],"device":[76,117],"convergence":[83,105],"quality,":[85],"then":[86],"propose":[87,107],"holistic":[89],"design":[90,97],"efficiently":[92],"sample":[93],"devices.":[94],"Furthermore,":[95],"dynamic":[99],"strategy":[100],"further":[102],"speed":[103],"FedAgg":[109],"algorithm":[110],"alleviate":[112],"deviation":[114],"caused":[115],"by":[116,139],"heterogeneity.":[118],"With":[119],"extensive":[120],"experiments":[121],"performed":[122],"PyTorch,":[124],"show":[126],"number":[129],"rounds":[132],"required":[133],"FL":[135],"can":[136],"reduced":[138],"52%":[142],"MNIST":[145],"dataset,":[146],"32%":[147],"CIFAR-10,":[149],"28%":[151],"FashionMNIST":[153],"comparison":[155],"Averaging":[159],"algorithm.":[160]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
