{"id":"https://openalex.org/W4385329459","doi":"https://doi.org/10.1109/iwqos57198.2023.10188807","title":"AdaCoOpt: Leverage the Interplay of Batch Size and Aggregation Frequency for Federated Learning","display_name":"AdaCoOpt: Leverage the Interplay of Batch Size and Aggregation Frequency for Federated Learning","publication_year":2023,"publication_date":"2023-06-19","ids":{"openalex":"https://openalex.org/W4385329459","doi":"https://doi.org/10.1109/iwqos57198.2023.10188807"},"language":"en","primary_location":{"id":"doi:10.1109/iwqos57198.2023.10188807","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwqos57198.2023.10188807","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS)","raw_type":"proceedings-article"},"type":"article","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/A5100668786","display_name":"Weijie Liu","orcid":"https://orcid.org/0000-0002-8023-9913"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weijie Liu","raw_affiliation_strings":["Sun Yat-sen University"],"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100763625","display_name":"Xiaoxi Zhang","orcid":"https://orcid.org/0000-0003-0751-2773"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoxi Zhang","raw_affiliation_strings":["Sun Yat-sen University"],"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016658545","display_name":"Jingpu Duan","orcid":"https://orcid.org/0000-0001-7507-2197"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingpu Duan","raw_affiliation_strings":["Pengcheng Laboratory"],"affiliations":[{"raw_affiliation_string":"Pengcheng Laboratory","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085243096","display_name":"Carlee Joe\u2010Wong","orcid":"https://orcid.org/0000-0003-0785-9291"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Carlee Joe-Wong","raw_affiliation_strings":["Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100760218","display_name":"Zhi Zhou","orcid":"https://orcid.org/0000-0002-0987-9344"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhi Zhou","raw_affiliation_strings":["Sun Yat-sen University"],"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100385692","display_name":"Xu Chen","orcid":"https://orcid.org/0000-0001-9943-6020"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Chen","raw_affiliation_strings":["Sun Yat-sen University"],"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100668786"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":1.049,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.81023443,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9976000189781189,"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":0.9901000261306763,"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":0.809459924697876},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.755412757396698},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6103411912918091},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.576514482498169},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5106845498085022},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.44406065344810486},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"Distributed learning","score":0.41399115324020386},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.34630143642425537},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.33922433853149414},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2649924159049988},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.25679850578308105}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.809459924697876},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.755412757396698},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6103411912918091},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.576514482498169},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5106845498085022},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.44406065344810486},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.41399115324020386},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.34630143642425537},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.33922433853149414},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2649924159049988},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25679850578308105},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwqos57198.2023.10188807","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwqos57198.2023.10188807","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[{"id":"https://openalex.org/G4403717979","display_name":null,"funder_award_id":"CNS-2106891,CNS-1751075","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4406670232","display_name":null,"funder_award_id":"62102460,202201011392,61972432","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W1652793671","https://openalex.org/W2296335794","https://openalex.org/W2604783387","https://openalex.org/W2769644379","https://openalex.org/W2769835638","https://openalex.org/W2921434559","https://openalex.org/W2955213239","https://openalex.org/W2963318081","https://openalex.org/W2963433607","https://openalex.org/W2975043678","https://openalex.org/W2981138228","https://openalex.org/W2982464076","https://openalex.org/W2995022099","https://openalex.org/W3038022836","https://openalex.org/W3043723611","https://openalex.org/W3046056026","https://openalex.org/W3047261146","https://openalex.org/W3047304572","https://openalex.org/W3091156884","https://openalex.org/W3093495284","https://openalex.org/W3099314130","https://openalex.org/W3125469145","https://openalex.org/W3134277527","https://openalex.org/W3156310591","https://openalex.org/W3174661481","https://openalex.org/W3174943464","https://openalex.org/W3178176830","https://openalex.org/W3183910508","https://openalex.org/W3184838508","https://openalex.org/W3187356235","https://openalex.org/W3203503583","https://openalex.org/W3204012185","https://openalex.org/W3211441304","https://openalex.org/W3214897310","https://openalex.org/W4205841652","https://openalex.org/W4226183928","https://openalex.org/W4285411375","https://openalex.org/W4287119847","https://openalex.org/W4288459131","https://openalex.org/W4318619660","https://openalex.org/W6636806777","https://openalex.org/W6697144307","https://openalex.org/W6728757088","https://openalex.org/W6736413256","https://openalex.org/W6746200960","https://openalex.org/W6759238902","https://openalex.org/W6760214840","https://openalex.org/W6765541894","https://openalex.org/W6768511045","https://openalex.org/W6781318954","https://openalex.org/W6784060510","https://openalex.org/W6784336702","https://openalex.org/W6791518594","https://openalex.org/W6795916790","https://openalex.org/W6797027800","https://openalex.org/W6798701190","https://openalex.org/W6798999553","https://openalex.org/W6799256697","https://openalex.org/W6803165305","https://openalex.org/W6811061068"],"related_works":["https://openalex.org/W2140186469","https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W4389724018","https://openalex.org/W4318719684","https://openalex.org/W3183136280","https://openalex.org/W4318559728","https://openalex.org/W2775233965","https://openalex.org/W4360995913","https://openalex.org/W4312193868"],"abstract_inverted_index":{"Federated":[0],"Learning":[1],"(FL)":[2],"is":[3],"a":[4,127,150,157],"distributed":[5],"learning":[6],"paradigm":[7],"that":[8,102,178],"can":[9,53,179],"coordinate":[10],"heterogeneous":[11,136],"edge":[12],"devices":[13,177],"to":[14,33,85,112,185,202],"perform":[15],"model":[16,56,183],"training":[17,58,133,137],"without":[18],"sharing":[19],"private":[20],"raw":[21],"data.":[22],"Many":[23],"prior":[24],"works":[25],"have":[26,78],"analyzed":[27],"the":[28,44,48,55,61,104,114,132,162,182,187,205,214],"FL":[29],"convergence":[30,129],"with":[31,89,207],"respect":[32],"important":[34],"hyperparameters,":[35],"including":[36],"batch":[37,45,107,152,174],"size":[38,46,108,153],"and":[39,47,60,66,71,81,99,109,119,154,192,221],"aggregation":[40,110,155],"frequency.":[41],"However,":[42],"adjusting":[43],"number":[49],"of":[50,63,106,131,149,189,216],"local":[51],"updates":[52],"affect":[54],"performance,":[57],"time,":[59],"cost":[62],"consuming":[64],"computation":[65],"communication":[67],"resources,":[68],"in":[69],"different":[70,173],"perhaps":[72],"complex":[73],"forms.":[74],"Their":[75],"joint":[76],"effects":[77],"been":[79],"overlooked":[80],"should":[82],"be":[83],"exploited":[84],"achieve":[86],"accurate":[87],"models":[88,98],"controllable":[90],"operational":[91],"expenditure.":[92],"This":[93],"paper":[94],"proposes":[95],"novel":[96],"analytical":[97],"optimization":[100],"algorithms":[101],"leverage":[103],"interplay":[105],"frequency":[111],"navigate":[113],"trade-offs":[115],"among":[116],"convergence,":[117],"cost,":[118],"completion":[120],"time":[121],"for":[122,160,171],"FL.":[123],"We":[124,164],"first":[125],"obtain":[126],"new":[128],"bound":[130],"error":[134],"under":[135],"datasets":[138],"across":[139,176],"devices.":[140,163],"Based":[141],"on":[142],"this":[143],"bound,":[144],"we":[145,196],"derive":[146],"closed-form":[147],"solutions":[148,206,220],"co-optimized":[151],"frequency,":[156],"single":[158],"configuration":[159],"all":[161],"then":[165],"design":[166],"an":[167,198],"efficient":[168],"exact":[169],"algorithm":[170,201],"assigning":[172],"configurations":[175],"further":[180],"improve":[181],"accuracy":[184],"address":[186],"heterogeneity":[188],"both":[190],"data":[191],"system":[193],"characteristics.":[194],"Further,":[195],"propose":[197],"adaptive":[199,223],"control":[200],"dynamically":[203],"adjust":[204],"estimated":[208],"network":[209],"states.":[210],"Extensive":[211],"experiments":[212],"demonstrate":[213],"superiority":[215],"our":[217],"offline":[218],"optimal":[219],"online":[222],"algorithm.":[224]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
