{"id":"https://openalex.org/W4292737460","doi":"https://doi.org/10.1145/3558005","title":"Personalized Federated Learning on Non-IID Data via Group-based Meta-learning","display_name":"Personalized Federated Learning on Non-IID Data via Group-based Meta-learning","publication_year":2022,"publication_date":"2022-08-23","ids":{"openalex":"https://openalex.org/W4292737460","doi":"https://doi.org/10.1145/3558005"},"language":"en","primary_location":{"id":"doi:10.1145/3558005","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3558005","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-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/A5050067325","display_name":"Lei Yang","orcid":"https://orcid.org/0000-0002-8732-3675"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Yang","raw_affiliation_strings":["South China University of Technology, China"],"raw_orcid":"https://orcid.org/0000-0002-8732-3675","affiliations":[{"raw_affiliation_string":"South China University of Technology, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100673237","display_name":"Jiaming Huang","orcid":"https://orcid.org/0000-0002-9752-8607"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaming Huang","raw_affiliation_strings":["South China University of Technology, China"],"raw_orcid":"https://orcid.org/0000-0002-9752-8607","affiliations":[{"raw_affiliation_string":"South China University of Technology, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046176565","display_name":"Wanyu Lin","orcid":"https://orcid.org/0000-0002-7328-8039"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Wanyu Lin","raw_affiliation_strings":["The Hong Kong Polytechnic University, China"],"raw_orcid":"https://orcid.org/0000-0002-7328-8039","affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University, China","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100740023","display_name":"Jiannong Cao","orcid":"https://orcid.org/0000-0002-2725-2529"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jiannong Cao","raw_affiliation_strings":["The Hong Kong Polytechnic University, China"],"raw_orcid":"https://orcid.org/0000-0002-2725-2529","affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University, China","institution_ids":["https://openalex.org/I14243506"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":13.4503,"has_fulltext":false,"cited_by_count":137,"citation_normalized_percentile":{"value":0.9913503,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"17","issue":"4","first_page":"1","last_page":"20"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.916700005531311,"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":0.80051589012146},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6942419409751892},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.6148462295532227},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.6028797030448914},{"id":"https://openalex.org/keywords/meta-learning","display_name":"Meta learning (computer science)","score":0.5182287693023682},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.5127790570259094},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5113968253135681},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47856953740119934},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.45625030994415283},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3529495596885681},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.11042135953903198}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.80051589012146},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6942419409751892},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.6148462295532227},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6028797030448914},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.5182287693023682},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.5127790570259094},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5113968253135681},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47856953740119934},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.45625030994415283},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3529495596885681},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.11042135953903198},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3558005","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3558005","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.47999998927116394,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[{"id":"https://openalex.org/G3928869049","display_name":"\u534f\u4f5c\u5f0f\u8fb9\u7f18\u8ba1\u7b97\u4e2d\u7f51\u7edc\u611f\u77e5\u7684\u4efb\u52a1\u8c03\u5ea6\u6280\u672f\u7814\u7a76","funder_award_id":"61972161","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7125625577","display_name":null,"funder_award_id":"2022A1515010374","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W191494004","https://openalex.org/W1583837637","https://openalex.org/W2187089797","https://openalex.org/W2602856279","https://openalex.org/W2604738573","https://openalex.org/W2734358244","https://openalex.org/W2912213068","https://openalex.org/W2995022099","https://openalex.org/W3018464563","https://openalex.org/W3021654819","https://openalex.org/W3042621011","https://openalex.org/W3080934299","https://openalex.org/W3091870957","https://openalex.org/W3133814152","https://openalex.org/W3145013517","https://openalex.org/W4200580682","https://openalex.org/W4206928648","https://openalex.org/W4231952741","https://openalex.org/W4255970238"],"related_works":["https://openalex.org/W4238897586","https://openalex.org/W435179959","https://openalex.org/W2619091065","https://openalex.org/W2059640416","https://openalex.org/W1490753184","https://openalex.org/W2284465472","https://openalex.org/W2291782699","https://openalex.org/W1993948687","https://openalex.org/W4303448918","https://openalex.org/W4386597570"],"abstract_inverted_index":{"Personalized":[0],"federated":[1,90,220],"learning":[2],"(PFL)":[3],"has":[4],"emerged":[5],"as":[6],"a":[7,11,42,50,97,135,183],"paradigm":[8],"to":[9,30,83,141,182,214,217],"provide":[10],"personalized":[12,51,122],"model":[13,52,210],"that":[14,68,152,165,205],"can":[15,169,179],"fit":[16],"the":[17,32,55,69,84,108,114,121,144,158,166,189,209,218],"local":[18,60],"data":[19,70,87,118,162],"distribution":[20,71,163],"of":[21,36,86,116,191],"each":[22,47,129,153],"client.":[23],"One":[24],"natural":[25],"choice":[26],"for":[27],"PFL":[28],"is":[29,75,155],"leverage":[31],"fast":[33],"adaptation":[34],"capability":[35],"meta-learning,":[37],"where":[38],"it":[39],"first":[40],"obtains":[41],"single":[43],"global":[44,56],"model,":[45],"and":[46,120],"client":[48],"achieves":[49],"by":[53,157,212],"fine-tuning":[54],"one":[57],"with":[58,126,160],"its":[59],"data.":[61],"However,":[62],"existing":[63],"meta-learning-based":[64],"approaches":[65],"implicitly":[66],"assume":[67],"among":[72],"different":[73],"clients":[74,109,145,159],"similar,":[76],"which":[77,105],"may":[78],"not":[79],"be":[80,180],"applicable":[81],"due":[82],"property":[85],"heterogeneity":[88],"in":[89],"learning.":[91],"In":[92,131],"this":[93],"work,":[94],"we":[95,133],"propose":[96],"Group-based":[98],"Federated":[99],"Meta-Learning":[100],"framework,":[101],"called":[102],"G-FML":[103,194],",":[104],"adaptively":[106,142],"divides":[107],"into":[110,146],"groups":[111],"based":[112],"on":[113,196],"similarity":[115],"their":[117],"distribution,":[119],"models":[123],"are":[124],"obtained":[125],"meta-learning":[127],"within":[128],"group.":[130],"particular,":[132],"develop":[134],"simple":[136],"yet":[137],"effective":[138],"grouping":[139],"mechanism":[140,150],"partition":[143],"multiple":[147],"groups.":[148],"Our":[149],"ensures":[151],"group":[154],"formed":[156],"similar":[161],"such":[164],"group-wise":[167],"meta-model":[168],"achieve":[170],"\u201cpersonalization\u201d":[171],"at":[172],"large.":[173],"By":[174],"doing":[175],"so,":[176],"our":[177,192,206],"framework":[178,195,207],"generalized":[181],"highly":[184],"heterogeneous":[185,198],"environment.":[186],"We":[187],"evaluate":[188],"effectiveness":[190],"proposed":[193],"three":[197],"benchmarking":[199],"datasets.":[200],"The":[201],"experimental":[202],"results":[203],"show":[204],"improves":[208],"accuracy":[211],"up":[213],"13.15%":[215],"relative":[216],"state-of-the-art":[219],"meta-learning.":[221]},"counts_by_year":[{"year":2026,"cited_by_count":21},{"year":2025,"cited_by_count":60},{"year":2024,"cited_by_count":36},{"year":2023,"cited_by_count":20}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2025-10-10T00:00:00"}
