{"id":"https://openalex.org/W7093314655","doi":"https://doi.org/10.1109/tmc.2025.3624064","title":"MAIR: Model Agnostic Instance Reweighing for Heterogeneous Federated Learning","display_name":"MAIR: Model Agnostic Instance Reweighing for Heterogeneous Federated Learning","publication_year":2025,"publication_date":"2025-10-22","ids":{"openalex":"https://openalex.org/W7093314655","doi":"https://doi.org/10.1109/tmc.2025.3624064"},"language":null,"primary_location":{"id":"doi:10.1109/tmc.2025.3624064","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmc.2025.3624064","pdf_url":null,"source":{"id":"https://openalex.org/S69141925","display_name":"IEEE Transactions on Mobile Computing","issn_l":"1536-1233","issn":["1536-1233","1558-0660","2161-9875"],"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 Mobile Computing","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":null,"display_name":"Dongping Liao","orcid":"https://orcid.org/0000-0003-4156-311X"},"institutions":[{"id":"https://openalex.org/I204512498","display_name":"University of Macau","ror":"https://ror.org/01r4q9n85","country_code":"MO","type":"education","lineage":["https://openalex.org/I204512498"]},{"id":"https://openalex.org/I6469544","display_name":"City University of Macau","ror":"https://ror.org/04gpd4q15","country_code":"MO","type":"education","lineage":["https://openalex.org/I6469544"]}],"countries":["MO"],"is_corresponding":true,"raw_author_name":"Dongping Liao","raw_affiliation_strings":["State Key Lab of IOTSC, University of Macau, Macau, China"],"affiliations":[{"raw_affiliation_string":"State Key Lab of IOTSC, University of Macau, Macau, China","institution_ids":["https://openalex.org/I6469544","https://openalex.org/I204512498"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xitong Gao","orcid":"https://orcid.org/0000-0002-2063-2051"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xitong Gao","raw_affiliation_strings":["Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":null,"display_name":"Chengzhong Xu","orcid":"https://orcid.org/0000-0001-9480-0356"},"institutions":[{"id":"https://openalex.org/I204512498","display_name":"University of Macau","ror":"https://ror.org/01r4q9n85","country_code":"MO","type":"education","lineage":["https://openalex.org/I204512498"]},{"id":"https://openalex.org/I6469544","display_name":"City University of Macau","ror":"https://ror.org/04gpd4q15","country_code":"MO","type":"education","lineage":["https://openalex.org/I6469544"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Chengzhong Xu","raw_affiliation_strings":["State Key Lab of IOTSC, University of Macau, Macau, China"],"affiliations":[{"raw_affiliation_string":"State Key Lab of IOTSC, University of Macau, Macau, China","institution_ids":["https://openalex.org/I6469544","https://openalex.org/I204512498"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I204512498","https://openalex.org/I6469544"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.75925358,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"25","issue":"3","first_page":"4241","last_page":"4252"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.8780999779701233,"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":0.8780999779701233,"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/T11719","display_name":"Data Quality and Management","score":0.013399999588727951,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.01269999984651804,"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/orchestration","display_name":"Orchestration","score":0.7235999703407288},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.6105999946594238},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5277000069618225},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.46369999647140503},{"id":"https://openalex.org/keywords/footprint","display_name":"Footprint","score":0.39239999651908875},{"id":"https://openalex.org/keywords/data-driven","display_name":"Data-driven","score":0.3619999885559082},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.3497999906539917},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.3481999933719635}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.887499988079071},{"id":"https://openalex.org/C199168358","wikidata":"https://www.wikidata.org/wiki/Q3367000","display_name":"Orchestration","level":3,"score":0.7235999703407288},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.6105999946594238},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5277000069618225},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.46369999647140503},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39989998936653137},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.39239999651908875},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3792000114917755},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.3619999885559082},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3596999943256378},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C2992317946","wikidata":"https://www.wikidata.org/wiki/Q712144","display_name":"De facto","level":2,"score":0.3465000092983246},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3458999991416931},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.33390000462532043},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.33329999446868896},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C5119721","wikidata":"https://www.wikidata.org/wiki/Q220501","display_name":"Quality of service","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.2603999972343445},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.25920000672340393}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmc.2025.3624064","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmc.2025.3624064","pdf_url":null,"source":{"id":"https://openalex.org/S69141925","display_name":"IEEE Transactions on Mobile Computing","issn_l":"1536-1233","issn":["1536-1233","1558-0660","2161-9875"],"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 Mobile Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1501130358","display_name":null,"funder_award_id":"62376263","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4386207735","display_name":null,"funder_award_id":"JCYJ20230807140507015","funder_id":"https://openalex.org/F4320326705","funder_display_name":"Science, Technology and Innovation Commission of Shenzhen Municipality"},{"id":"https://openalex.org/G5840464552","display_name":null,"funder_award_id":"2023B1515130002","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G7362547439","display_name":null,"funder_award_id":"2024A1515030209","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science 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/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null},{"id":"https://openalex.org/F4320322841","display_name":"Universidade de Macau","ror":"https://ror.org/01r4q9n85"},{"id":"https://openalex.org/F4320326705","display_name":"Science, Technology and Innovation Commission of Shenzhen Municipality","ror":null},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1590183771","https://openalex.org/W1861492603","https://openalex.org/W2108598243","https://openalex.org/W2156909104","https://openalex.org/W2163922914","https://openalex.org/W2167101736","https://openalex.org/W2440599146","https://openalex.org/W2614367549","https://openalex.org/W2734358244","https://openalex.org/W2744999500","https://openalex.org/W2963691377","https://openalex.org/W3182158470","https://openalex.org/W4361859665","https://openalex.org/W4385627126","https://openalex.org/W4386083041","https://openalex.org/W4387717607","https://openalex.org/W4391806964","https://openalex.org/W4392901698","https://openalex.org/W4392979593","https://openalex.org/W4405022532","https://openalex.org/W4409367320"],"related_works":[],"abstract_inverted_index":{"Federated":[0],"learning":[1],"(FL)":[2],"enables":[3],"collaborative":[4],"train":[5],"ing":[6],"on":[7,73,146,192,201],"decentralized":[8],"data":[9,13,43,77,117,148,240],"while":[10],"preserving":[11],"the":[12,17,79,108,116,126,174,204,213],"owners'":[14],"privacy,":[15],"under":[16,239],"orchestration":[18],"of":[19,128,176,215],"a":[20,38,47,52,95,103,120,133,138,184,233],"central":[21],"server.":[22],"FL":[23,36,238],"has":[24],"seen":[25],"tremendous":[26],"growth":[27],"and":[28,51,149,172,195,224],"advancements":[29],"in":[30,237],"recent":[31],"years.":[32],"Despite":[33],"its":[34,158,180],"progress,":[35],"faces":[37],"significant":[39],"challenge":[40],"raised":[41],"by":[42,183],"heterogeneity,":[44],"leading":[45],"to":[46,57,114],"slower":[48],"convergence":[49,161,171],"rate":[50],"larger":[53],"performance":[54,214],"gap":[55],"compared":[56],"centralized":[58],"training.":[59],"In":[60],"this":[61,91],"work,":[62],"we":[63,93,156],"empirically":[64],"reveal":[65],"that":[66,166,211],"direct":[67],"applying":[68],"empirical":[69],"risk":[70],"minimizing":[71],"(ERM)":[72],"skewed":[74],"client":[75,80,142,153],"training":[76,130],"causes":[78],"model":[81,96],"suffers":[82],"from":[83,144],"biased":[84],"predictions":[85,110],"towards":[86],"majority":[87],"classes.":[88],"To":[89],"address":[90],"problem,":[92],"propose":[94],"agnostic":[97],"instance":[98],"reweighing":[99],"method":[100],"(MAIR).":[101],"At":[102,119],"coarse-grained":[104],"level,":[105,122],"MAIR":[106,140,167],"adjusts":[107],"logits":[109],"for":[111],"each":[112],"class":[113],"counteract":[115],"heterogeneity.":[118,241],"fine-grained":[121],"it":[123],"dynamically":[124],"reweighs":[125],"importance":[127],"individual":[129],"samples":[131],"with":[132],"predictive":[134],"meta":[135],"network.":[136],"As":[137],"results,":[139],"prevents":[141],"models":[143,217],"over-fitting":[145],"heterogeneous":[147],"therefore":[150],"substantially":[151],"reduces":[152],"drift.":[154],"Theoretically,":[155],"justify":[157],"non":[159],"convex":[160],"property.":[162],"Extensive":[163],"experiments":[164],"demonstrate":[165],"reliably":[168],"speeds":[169],"up":[170],"improves":[173],"quality":[175],"global":[177],"models,":[178],"outperforming":[179],"best":[181],"competitor":[182],"clear":[185],"margin.":[186],"It":[187],"notably":[188],"delivers":[189],"8.3%":[190],"improvements":[191],"ImageNet":[193],"subset":[194],"achieves":[196],"67.6%":[197],"energy":[198],"footprint":[199],"reduction":[200],"CIFAR-100":[202],"over":[203],"FedAvg":[205],"baseline.":[206],"Our":[207],"findings":[208],"also":[209],"suggest":[210],"improving":[212],"FL-trained":[216],"necessitates":[218],"rethinking":[219],"clients'":[220],"local":[221],"optimization":[222],"objectives,":[223],"ERM":[225],"should":[226],"thus":[227],"no":[228],"longer":[229],"be":[230],"viewed":[231],"as":[232],"de":[234],"facto":[235],"standard":[236]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-24T00:00:00"}
