{"id":"https://openalex.org/W7117475908","doi":"https://doi.org/10.1109/tnse.2025.3649075","title":"SCALA: Split Federated Learning With Concatenated Activations and Logit Adjustments","display_name":"SCALA: Split Federated Learning With Concatenated Activations and Logit Adjustments","publication_year":2025,"publication_date":"2025-12-29","ids":{"openalex":"https://openalex.org/W7117475908","doi":"https://doi.org/10.1109/tnse.2025.3649075"},"language":null,"primary_location":{"id":"doi:10.1109/tnse.2025.3649075","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnse.2025.3649075","pdf_url":null,"source":{"id":"https://openalex.org/S2484352698","display_name":"IEEE Transactions on Network Science and Engineering","issn_l":"2327-4697","issn":["2327-4697","2334-329X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Network Science and Engineering","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/A5019105341","display_name":"Jiarong Yang","orcid":"https://orcid.org/0000-0003-1273-4274"},"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":"Jiarong Yang","raw_affiliation_strings":["School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-1273-4274","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yuan Liu","orcid":"https://orcid.org/0000-0002-9447-0219"},"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":"Yuan Liu","raw_affiliation_strings":["School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-9447-0219","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.745645,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"5345","last_page":"5362"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.5088000297546387,"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":0.5088000297546387,"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":0.10040000081062317,"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/T14347","display_name":"Big Data and Digital Economy","score":0.03440000116825104,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/concatenation","display_name":"Concatenation (mathematics)","score":0.8381999731063843},{"id":"https://openalex.org/keywords/logit","display_name":"Logit","score":0.6686000227928162},{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.4733000099658966},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4422000050544739},{"id":"https://openalex.org/keywords/mixed-logit","display_name":"Mixed logit","score":0.43059998750686646},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.420199990272522}],"concepts":[{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.8381999731063843},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7712000012397766},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.6686000227928162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4837999939918518},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.4733000099658966},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4510999917984009},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4422000050544739},{"id":"https://openalex.org/C95057490","wikidata":"https://www.wikidata.org/wiki/Q6883984","display_name":"Mixed logit","level":3,"score":0.43059998750686646},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.420199990272522},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38440001010894775},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.32659998536109924},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3116999864578247},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.2547999918460846}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tnse.2025.3649075","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnse.2025.3649075","pdf_url":null,"source":{"id":"https://openalex.org/S2484352698","display_name":"IEEE Transactions on Network Science and Engineering","issn_l":"2327-4697","issn":["2327-4697","2334-329X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Network Science and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2744999500","https://openalex.org/W2963209930","https://openalex.org/W2995022099","https://openalex.org/W2999074226","https://openalex.org/W3004277316","https://openalex.org/W3009048827","https://openalex.org/W3018102029","https://openalex.org/W3021654819","https://openalex.org/W3043758338","https://openalex.org/W3119680904","https://openalex.org/W3129329365","https://openalex.org/W3129519326","https://openalex.org/W3140722389","https://openalex.org/W3176474016","https://openalex.org/W3177931007","https://openalex.org/W3196371845","https://openalex.org/W3198770621","https://openalex.org/W3202544039","https://openalex.org/W3204874618","https://openalex.org/W3207233526","https://openalex.org/W3211848727","https://openalex.org/W4226128266","https://openalex.org/W4229029907","https://openalex.org/W4244201901","https://openalex.org/W4283796083","https://openalex.org/W4285603347","https://openalex.org/W4312815981","https://openalex.org/W4319654190","https://openalex.org/W4319988669","https://openalex.org/W4366352743","https://openalex.org/W4385245566","https://openalex.org/W4385338532","https://openalex.org/W4386280917","https://openalex.org/W4387353472","https://openalex.org/W4387717607","https://openalex.org/W4388145809","https://openalex.org/W4389371065","https://openalex.org/W4390577867","https://openalex.org/W4392910504","https://openalex.org/W4393058305","https://openalex.org/W4393159708","https://openalex.org/W4396240126","https://openalex.org/W4396712758","https://openalex.org/W4400910543","https://openalex.org/W4402703097","https://openalex.org/W4402754207","https://openalex.org/W4402780394","https://openalex.org/W4404035289","https://openalex.org/W4404563172","https://openalex.org/W4409363285","https://openalex.org/W4411194660","https://openalex.org/W4413147265","https://openalex.org/W4415482297","https://openalex.org/W7083004866"],"related_works":[],"abstract_inverted_index":{"Split":[0],"Federated":[1],"Learning":[2],"(SFL)":[3],"is":[4],"a":[5],"distributed":[6],"machine":[7],"learning":[8,37],"framework":[9],"where":[10],"the":[11,18,62,65,80,94,108,113,125,131,135],"models":[12,58,88],"are":[13,59,89],"split":[14],"and":[15,20,25,49,76,86],"trained":[16],"on":[17],"server":[19],"clients.":[21,103],"However,":[22],"data":[23,116],"heterogeneity":[24],"partial":[26],"client":[27],"participation":[28],"result":[29],"in":[30,53,79,121],"label":[31,71,95],"distribution":[32,72,96],"skew,":[33],"which":[34,54],"severely":[35],"degrades":[36],"performance.":[38],"To":[39],"address":[40],"this":[41],"issue,":[42],"we":[43],"propose":[44],"SFL":[45],"with":[46,93],"Concatenated":[47],"Activations":[48],"Logit":[50],"Adjustments":[51],"(SCALA),":[52],"activations":[55,111],"from":[56],"client-side":[57,87],"concatenated":[60],"as":[61],"input":[63],"of":[64,83,101,110,115,127,133,137],"server-side":[66,85],"model":[67],"to":[68,91],"centrally":[69],"adjust":[70],"across":[73,98],"different":[74,99],"clients,":[75],"logit":[77,119],"adjustments":[78,120],"loss":[81,122],"functions":[82,123],"both":[84],"performed":[90],"deal":[92],"variation":[97],"subsets":[100],"participating":[102],"Theoretical":[104],"analysis":[105],"demonstrates":[106],"that":[107],"concatenation":[109],"reduces":[112],"impact":[114],"heterogeneity,":[117],"while":[118],"enhance":[124],"recognition":[126,136],"low-frequency":[128],"labels":[129],"at":[130],"cost":[132],"sacrificing":[134],"high-frequency":[138],"labels.":[139]},"counts_by_year":[],"updated_date":"2026-01-17T23:10:49.606395","created_date":"2025-12-29T00:00:00"}
