{"id":"https://openalex.org/W7108612892","doi":"https://doi.org/10.1145/3742874.3756007","title":"Enabling Skew-aware Federated Learning on Embedded Systems via Non-IID Data Distribution Type Estimation","display_name":"Enabling Skew-aware Federated Learning on Embedded Systems via Non-IID Data Distribution Type Estimation","publication_year":2025,"publication_date":"2025-09-28","ids":{"openalex":"https://openalex.org/W7108612892","doi":"https://doi.org/10.1145/3742874.3756007"},"language":null,"primary_location":{"id":"doi:10.1145/3742874.3756007","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3742874.3756007","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3742874.3756007","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Embedded Software","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3742874.3756007","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Tatsuya Nishio","orcid":"https://orcid.org/0009-0006-9579-4449"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tatsuya Nishio","raw_affiliation_strings":["The University of Osaka, Osaka, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Osaka, Osaka, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hiroki NIshikawa","orcid":"https://orcid.org/0000-0002-9626-0944"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hiroki NIshikawa","raw_affiliation_strings":["The University of Osaka, Osaka, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Osaka, Osaka, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ittetsu Taniguchi","orcid":"https://orcid.org/0000-0002-7843-5907"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ittetsu Taniguchi","raw_affiliation_strings":["The University of Osaka, Osaka, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Osaka, Osaka, Japan","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Takao Onoye","orcid":"https://orcid.org/0000-0002-1894-2448"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Takao Onoye","raw_affiliation_strings":["The University of Osaka, Osaka, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Osaka, Osaka, Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.79299589,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.36579999327659607,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.36579999327659607,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.17579999566078186,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.10339999943971634,"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/mnist-database","display_name":"MNIST database","score":0.796999990940094},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.703000009059906},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.6097000241279602},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5874999761581421},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5679000020027161},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.45509999990463257},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.41749998927116394},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.396699994802475},{"id":"https://openalex.org/keywords/data-sharing","display_name":"Data sharing","score":0.3961000144481659}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7997000217437744},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.796999990940094},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.703000009059906},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.6097000241279602},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5874999761581421},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5679000020027161},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49639999866485596},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4918999969959259},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.45509999990463257},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44859999418258667},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.41749998927116394},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.396699994802475},{"id":"https://openalex.org/C2779965156","wikidata":"https://www.wikidata.org/wiki/Q5227350","display_name":"Data sharing","level":3,"score":0.3961000144481659},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.3874000012874603},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.37880000472068787},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.35179999470710754},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.34540000557899475},{"id":"https://openalex.org/C2777299769","wikidata":"https://www.wikidata.org/wiki/Q3707858","display_name":"Type (biology)","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3742874.3756007","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3742874.3756007","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3742874.3756007","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Embedded Software","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3742874.3756007","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3742874.3756007","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3742874.3756007","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Embedded Software","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5332326889038086,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G1069223013","display_name":null,"funder_award_id":"JSPS KAKENHI","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G2548931388","display_name":null,"funder_award_id":"23K16858","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G3459562248","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G4636223006","display_name":null,"funder_award_id":"JSPS KAK","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G8430481527","display_name":null,"funder_award_id":"Number","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7108612892.pdf","grobid_xml":"https://content.openalex.org/works/W7108612892.grobid-xml"},"referenced_works_count":8,"referenced_works":["https://openalex.org/W3080934299","https://openalex.org/W3163514073","https://openalex.org/W3194243671","https://openalex.org/W4200631596","https://openalex.org/W4229029907","https://openalex.org/W4312869277","https://openalex.org/W4323338449","https://openalex.org/W4393973089"],"related_works":[],"abstract_inverted_index":{"Federated":[0],"learning":[1],"enables":[2],"decentralized":[3],"training":[4],"without":[5,99],"sharing":[6],"raw":[7,102],"data,":[8],"making":[9],"it":[10],"suitable":[11],"for":[12],"privacy":[13],"aware":[14],"applications.":[15],"However,":[16],"its":[17],"performance":[18],"often":[19],"degrades":[20],"in":[21,62],"real":[22],"settings":[23,124],"due":[24],"to":[25,112],"unknown":[26],"and":[27,152],"diverse":[28],"data":[29,56,76],"differences":[30],"among":[31],"clients.":[32],"While":[33],"many":[34],"mitigation":[35],"strategies":[36],"have":[37],"been":[38],"proposed,":[39],"they":[40],"typically":[41],"assume":[42],"prior":[43],"knowledge":[44],"of":[45,70,75,141],"the":[46,67,72,91,118,127,139],"imbalance":[47,92,123],"type,":[48],"such":[49],"as":[50,145],"feature":[51],"variation,":[52],"label":[53],"bias,":[54],"or":[55],"quantity":[57],"differences,":[58],"which":[59,89],"is":[60],"unrealistic":[61],"practice.":[63],"This":[64,137],"paper":[65],"addresses":[66],"overlooked":[68],"problem":[69],"identifying":[71],"main":[73],"type":[74,93,143],"imbalance.":[77],"We":[78],"propose":[79],"a":[80,146],"method":[81,129],"called":[82],"Machine":[83],"Learning":[84],"based":[85],"Non":[86],"IID":[87],"Estimator,":[88],"classifies":[90],"by":[94],"analyzing":[95],"trained":[96],"client":[97],"models":[98],"accessing":[100],"any":[101],"data.":[103],"Similarity":[104],"matrices":[105],"computed":[106],"from":[107],"model":[108],"parameters":[109],"are":[110],"used":[111],"train":[113],"standard":[114],"classifiers.":[115],"Evaluations":[116],"on":[117],"MNIST":[119],"dataset":[120],"under":[121],"controlled":[122],"show":[125],"that":[126],"proposed":[128],"achieves":[130],"perfect":[131],"classification":[132],"accuracy":[133],"with":[134],"lightweight":[135],"models.":[136],"highlights":[138],"potential":[140],"distribution":[142],"estimation":[144],"key":[147],"step":[148],"toward":[149],"more":[150],"robust":[151],"efficient":[153],"federated":[154],"systems.":[155]},"counts_by_year":[],"updated_date":"2026-03-25T14:56:36.534964","created_date":"2025-12-05T00:00:00"}
