{"id":"https://openalex.org/W4405022380","doi":"https://doi.org/10.26599/bdma.2024.9020065","title":"A Remedy for Heterogeneous Data: Clustered Federated Learning with Gradient Trajectory","display_name":"A Remedy for Heterogeneous Data: Clustered Federated Learning with Gradient Trajectory","publication_year":2024,"publication_date":"2024-12-01","ids":{"openalex":"https://openalex.org/W4405022380","doi":"https://doi.org/10.26599/bdma.2024.9020065"},"language":"en","primary_location":{"id":"doi:10.26599/bdma.2024.9020065","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2024.9020065","pdf_url":null,"source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.26599/bdma.2024.9020065","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100737797","display_name":"Ruiqi Liu","orcid":"https://orcid.org/0009-0004-6347-3179"},"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":"Ruiqi Liu","raw_affiliation_strings":["School of Intelligent Systems Engineering, Sun Yat-Sen University,Shenzhen,China,510275"],"affiliations":[{"raw_affiliation_string":"School of Intelligent Systems Engineering, Sun Yat-Sen University,Shenzhen,China,510275","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013663621","display_name":"Songcan Yu","orcid":"https://orcid.org/0000-0003-2991-3703"},"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":"Songcan Yu","raw_affiliation_strings":["School of Intelligent Systems Engineering, Sun Yat-Sen University,Shenzhen,China,510275"],"affiliations":[{"raw_affiliation_string":"School of Intelligent Systems Engineering, Sun Yat-Sen University,Shenzhen,China,510275","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102872724","display_name":"Linsi Lan","orcid":"https://orcid.org/0009-0004-9824-419X"},"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":"Linsi Lan","raw_affiliation_strings":["School of Intelligent Systems Engineering, Sun Yat-Sen University,Shenzhen,China,510275"],"affiliations":[{"raw_affiliation_string":"School of Intelligent Systems Engineering, Sun Yat-Sen University,Shenzhen,China,510275","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026553438","display_name":"Junbo Wang","orcid":"https://orcid.org/0000-0002-2748-8953"},"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":"Junbo Wang","raw_affiliation_strings":["School of Intelligent Systems Engineering, Sun Yat-Sen University,Shenzhen,China,510275"],"affiliations":[{"raw_affiliation_string":"School of Intelligent Systems Engineering, Sun Yat-Sen University,Shenzhen,China,510275","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008563545","display_name":"Krishna Kant","orcid":"https://orcid.org/0000-0001-5743-9944"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Krishna Kant","raw_affiliation_strings":["Temple University,Computer and Information Systems Department,Philadelphia,AZ,USA,19122"],"affiliations":[{"raw_affiliation_string":"Temple University,Computer and Information Systems Department,Philadelphia,AZ,USA,19122","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087764352","display_name":"Neville Calleja","orcid":"https://orcid.org/0000-0003-1800-2103"},"institutions":[{"id":"https://openalex.org/I197854408","display_name":"University of Malta","ror":"https://ror.org/03a62bv60","country_code":"MT","type":"education","lineage":["https://openalex.org/I197854408"]}],"countries":["MT"],"is_corresponding":false,"raw_author_name":"Neville Calleja","raw_affiliation_strings":["University of Malta,Department of Policy in Health,Msida,MSD,Malta,2080"],"affiliations":[{"raw_affiliation_string":"University of Malta,Department of Policy in Health,Msida,MSD,Malta,2080","institution_ids":["https://openalex.org/I197854408"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100737797"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":3.6476,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":{"value":0.93978677,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"7","issue":"4","first_page":"1050","last_page":"1064"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9977999925613403,"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":0.9977999925613403,"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":0.7961899042129517},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6663402915000916},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.6587249636650085},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5141749382019043},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4600517451763153},{"id":"https://openalex.org/keywords/mainstream","display_name":"Mainstream","score":0.4462856650352478},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.426835298538208},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39191222190856934},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34071630239486694}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7961899042129517},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6663402915000916},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6587249636650085},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5141749382019043},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4600517451763153},{"id":"https://openalex.org/C2777617010","wikidata":"https://www.wikidata.org/wiki/Q18957","display_name":"Mainstream","level":2,"score":0.4462856650352478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.426835298538208},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39191222190856934},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34071630239486694},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","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/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.26599/bdma.2024.9020065","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2024.9020065","pdf_url":null,"source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},{"id":"pmh:oai:www.um.edu.mt:123456789/128884","is_oa":true,"landing_page_url":"https://www.um.edu.mt/library/oar/handle/123456789/128884","pdf_url":"https://www.um.edu.mt/library/oar/bitstream/123456789/128884/1/A_remedy_for_heterogeneous_data.pdf","source":{"id":"https://openalex.org/S4306400782","display_name":"OAR@UM (University of Malta)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I197854408","host_organization_name":"University of Malta","host_organization_lineage":["https://openalex.org/I197854408"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:9778506c8f504137bb2b0851bbb5052a","is_oa":true,"landing_page_url":"https://doaj.org/article/9778506c8f504137bb2b0851bbb5052a","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Big Data Mining and Analytics, Vol 7, Iss 4, Pp 1050-1064 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.26599/bdma.2024.9020065","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2024.9020065","pdf_url":null,"source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2851562800","display_name":null,"funder_award_id":"62072485","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5096041344","display_name":null,"funder_award_id":"2022A1515011294","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":43,"referenced_works":["https://openalex.org/W314265539","https://openalex.org/W2100060407","https://openalex.org/W2158703410","https://openalex.org/W2342783452","https://openalex.org/W2591882872","https://openalex.org/W2605102252","https://openalex.org/W2618530766","https://openalex.org/W2900594532","https://openalex.org/W2946417913","https://openalex.org/W2963456518","https://openalex.org/W2995022099","https://openalex.org/W3021654819","https://openalex.org/W3048492641","https://openalex.org/W3080934299","https://openalex.org/W3089578458","https://openalex.org/W3091635927","https://openalex.org/W3091870957","https://openalex.org/W3168213397","https://openalex.org/W3214919557","https://openalex.org/W4200580682","https://openalex.org/W4287332481","https://openalex.org/W6682132143","https://openalex.org/W6728757088","https://openalex.org/W6735236233","https://openalex.org/W6749892895","https://openalex.org/W6764838729","https://openalex.org/W6765541894","https://openalex.org/W6766776083","https://openalex.org/W6768632158","https://openalex.org/W6773039429","https://openalex.org/W6774195376","https://openalex.org/W6775563089","https://openalex.org/W6776372214","https://openalex.org/W6779269186","https://openalex.org/W6784336702","https://openalex.org/W6789305514","https://openalex.org/W6789582715","https://openalex.org/W6791102956","https://openalex.org/W6791182590","https://openalex.org/W6791444617","https://openalex.org/W6795843344","https://openalex.org/W6803975504","https://openalex.org/W6810700234"],"related_works":["https://openalex.org/W1583826057","https://openalex.org/W2377237701","https://openalex.org/W2360099860","https://openalex.org/W4323893170","https://openalex.org/W2352463596","https://openalex.org/W2380850119","https://openalex.org/W2101450440","https://openalex.org/W2383675217","https://openalex.org/W2376151201","https://openalex.org/W2393898889"],"abstract_inverted_index":{"Federated":[0,128],"Learning":[1,129],"(FL)":[2],"has":[3],"recently":[4],"attracted":[5],"a":[6,16,70,90],"lot":[7],"of":[8,41,45,73,125],"attention":[9],"due":[10],"to":[11,14,118],"its":[12,81],"ability":[13],"train":[15],"machine":[17],"learning":[18,94],"model":[19],"using":[20],"data":[21,32,59],"from":[22,69,80],"multiple":[23],"clients":[24,34,67,104],"without":[25],"divulging":[26],"their":[27],"privacy.":[28],"However,":[29],"the":[30,55,66,108],"training":[31],"across":[33],"can":[35,57],"be":[36],"very":[37],"heterogeneous":[38],"in":[39],"terms":[40],"quality,":[42],"amount,":[43],"occurrences":[44],"specific":[46],"features,":[47],"etc.":[48],"In":[49],"this":[50],"paper,":[51],"we":[52,88],"demonstrate":[53],"how":[54],"server":[56],"observe":[58],"heterogeneity":[60],"by":[61,77],"mining":[62],"gradient":[63,96,109],"trajectories":[64],"that":[65,120],"compute":[68],"two-dimensional":[71],"mapping":[72],"high-dimensional":[74],"gradients":[75],"computed":[76],"each":[78],"client":[79],"bottom":[82],"layer.":[83],"Based":[84],"on":[85,107],"these":[86],"ideas,":[87],"propose":[89],"new":[91],"clustered":[92],"federated":[93],"with":[95],"trajectory":[97],"method,":[98],"called":[99],"CFLGT,":[100],"which":[101],"dynamically":[102],"clusters":[103],"together":[105],"based":[106],"trajectories.":[110],"We":[111],"analyze":[112],"CFLGT":[113],"both":[114],"theoretically":[115],"and":[116,132],"experimentally":[117],"show":[119],"it":[121],"overcomes":[122],"several":[123],"drawbacks":[124],"mainstream":[126],"Clustered":[127],"(CFL)":[130],"methods":[131],"outperforms":[133],"other":[134],"baselines.":[135]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":6}],"updated_date":"2026-04-23T09:07:50.710637","created_date":"2025-10-10T00:00:00"}
