{"id":"https://openalex.org/W3159481909","doi":"https://doi.org/10.1109/tpami.2021.3129809","title":"Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning","display_name":"Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning","publication_year":2021,"publication_date":"2021-11-23","ids":{"openalex":"https://openalex.org/W3159481909","doi":"https://doi.org/10.1109/tpami.2021.3129809","mag":"3159481909","pmid":"https://pubmed.ncbi.nlm.nih.gov/34813467"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2021.3129809","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2021.3129809","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5084652371","display_name":"Song\u2010Nam Hong","orcid":"https://orcid.org/0000-0002-9535-2521"},"institutions":[{"id":"https://openalex.org/I4575257","display_name":"Hanyang University","ror":"https://ror.org/046865y68","country_code":"KR","type":"education","lineage":["https://openalex.org/I4575257"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Songnam Hong","raw_affiliation_strings":["Department of Electronic Engineering, Hanyang University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Hanyang University, Seoul, Korea","institution_ids":["https://openalex.org/I4575257"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001728130","display_name":"Jeongmin Chae","orcid":"https://orcid.org/0000-0001-6007-1300"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeongmin Chae","raw_affiliation_strings":["Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5084652371"],"corresponding_institution_ids":["https://openalex.org/I4575257"],"apc_list":null,"apc_paid":null,"fwci":2.1755,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.895398,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"44","issue":"12","first_page":"9872","last_page":"9886"},"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":0.9886999726295471,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9871000051498413,"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":0.7500008344650269},{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.6685804724693298},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.598704993724823},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.5914471745491028},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5728168487548828},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5199838280677795},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.49133506417274475},{"id":"https://openalex.org/keywords/sublinear-function","display_name":"Sublinear function","score":0.4772360324859619},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.4143889248371124},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3659052848815918},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36475569009780884},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.32495754957199097},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1833791434764862},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.10242125391960144}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7500008344650269},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.6685804724693298},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.598704993724823},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.5914471745491028},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5728168487548828},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5199838280677795},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49133506417274475},{"id":"https://openalex.org/C117160843","wikidata":"https://www.wikidata.org/wiki/Q338652","display_name":"Sublinear function","level":2,"score":0.4772360324859619},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.4143889248371124},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3659052848815918},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36475569009780884},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32495754957199097},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1833791434764862},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.10242125391960144},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003142","descriptor_name":"Communication","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003142","descriptor_name":"Communication","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003142","descriptor_name":"Communication","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D020375","descriptor_name":"Education, Distance","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D020375","descriptor_name":"Education, Distance","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D020375","descriptor_name":"Education, Distance","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2021.3129809","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2021.3129809","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:34813467","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34813467","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W134960717","https://openalex.org/W1510073064","https://openalex.org/W1560724230","https://openalex.org/W2040731319","https://openalex.org/W2109743529","https://openalex.org/W2121269968","https://openalex.org/W2131496831","https://openalex.org/W2139320579","https://openalex.org/W2144902422","https://openalex.org/W2150621701","https://openalex.org/W2513180554","https://openalex.org/W2594297742","https://openalex.org/W2615761526","https://openalex.org/W2766140019","https://openalex.org/W2769644379","https://openalex.org/W2807006176","https://openalex.org/W2900120080","https://openalex.org/W2907828870","https://openalex.org/W2912099989","https://openalex.org/W2912213068","https://openalex.org/W2963664311","https://openalex.org/W2963766684","https://openalex.org/W2963798770","https://openalex.org/W2963976431","https://openalex.org/W2981206218","https://openalex.org/W2982475424","https://openalex.org/W3013860853","https://openalex.org/W3038022836","https://openalex.org/W3102310167","https://openalex.org/W3120740533","https://openalex.org/W3193777527","https://openalex.org/W3195149063","https://openalex.org/W4294106961","https://openalex.org/W4297685247","https://openalex.org/W4300427714","https://openalex.org/W6605479355","https://openalex.org/W6681302627","https://openalex.org/W6734520927","https://openalex.org/W6738383168","https://openalex.org/W6745723224","https://openalex.org/W6746200960","https://openalex.org/W6748019269","https://openalex.org/W6748213982","https://openalex.org/W6752029299","https://openalex.org/W6754341472","https://openalex.org/W6755988804","https://openalex.org/W6757969384","https://openalex.org/W6759238902","https://openalex.org/W6769624030","https://openalex.org/W6771536673","https://openalex.org/W6779445818"],"related_works":["https://openalex.org/W4376155396","https://openalex.org/W2971351794","https://openalex.org/W1947085858","https://openalex.org/W2101991911","https://openalex.org/W2174986909","https://openalex.org/W2527791220","https://openalex.org/W2155070487","https://openalex.org/W4311589891","https://openalex.org/W3039767703","https://openalex.org/W3205806653"],"abstract_inverted_index":{"Online":[0],"federated":[1,44,117],"learning":[2,198],"(OFL)":[3],"is":[4,58,83,122],"a":[5,10,28,70,96,125,165],"promising":[6],"framework":[7],"to":[8,55,123,135],"learn":[9],"sequence":[11],"of":[12,95,157],"global":[13],"functions":[14],"from":[15],"distributed":[16],"sequential":[17],"data":[18],"at":[19],"local":[20],"devices.":[21],"In":[22],"this":[23,100],"framework,":[24],"we":[25,68,163,182],"first":[26],"introduce":[27],"single":[29],"kernel-based":[30],"OFL":[31],"(termed":[32,74],"S-KOFL)":[33],"by":[34],"incorporating":[35],"random-feature":[36],"(RF)":[37],"approximation,":[38],"online":[39,197],"gradient":[40],"descent":[41],"(OGD),":[42],"and":[43,76,112],"averaging":[45],"(FedAvg).":[46],"As":[47],"manifested":[48],"in":[49,64,114,159],"the":[50,61,65,87,93,106,115,154,170,187],"centralized":[51,66],"counterpart,":[52],"an":[53,148,160],"extension":[54,62],"multi-kernel":[56,72],"method":[57],"necessary.":[59],"Harnessing":[60],"principle":[63],"method,":[67],"construct":[69],"vanilla":[71],"algorithm":[73,128],"vM-KOFL)":[75],"prove":[77,144],"its":[78],"asymptotic":[79],"optimality.":[80],"However,":[81],"it":[82],"not":[84],"practical":[85,167],"as":[86,174,191],"communication":[88,140,172],"overhead":[89,173],"grows":[90],"linearly":[91],"with":[92,179],"size":[94],"kernel":[97],"dictionary.":[98],"Moreover,":[99],"problem":[101],"cannot":[102],"be":[103],"addressed":[104],"via":[105],"existing":[107],"communication-efficient":[108],"techniques":[109],"(e.g.,":[110],"quantization":[111],"sparsification)":[113],"conventional":[116],"learning.":[118],"Our":[119],"major":[120],"contribution":[121],"propose":[124,164],"novel":[126],"randomized":[127],"(named":[129],"eM-KOFL),":[130],"which":[131],"exhibits":[132],"similar":[133],"performance":[134,190],"vM-KOFL":[136,192],"while":[137],"maintaining":[138],"low":[139],"cost.":[141],"We":[142],"theoretically":[143],"that":[145,184],"eM-KOFL":[146,158],"achieves":[147],"optimal":[149],"sublinear":[150],"regret":[151],"bound.":[152],"Mimicking":[153],"key":[155],"concept":[156],"efficient":[161],"way,":[162],"more":[166],"pM-KOFL":[168,185],"having":[169],"same":[171,189],"S-KOFL.":[175],"Via":[176],"numerical":[177],"tests":[178],"real":[180],"datasets,":[181],"demonstrate":[183],"yields":[186],"almost":[188],"(or":[193],"eM-KOFL)":[194],"on":[195],"various":[196],"tasks.":[199]},"counts_by_year":[{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
