{"id":"https://openalex.org/W4417284634","doi":"https://doi.org/10.1109/pimrc62392.2025.11274983","title":"Concatenated Activations Enabled Split Federated Learning with Logit Adjustments","display_name":"Concatenated Activations Enabled Split Federated Learning with Logit Adjustments","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4417284634","doi":"https://doi.org/10.1109/pimrc62392.2025.11274983"},"language":null,"primary_location":{"id":"doi:10.1109/pimrc62392.2025.11274983","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc62392.2025.11274983","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 36th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","raw_type":"proceedings-article"},"type":"conference-paper","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":["South China University of Technology,School of Electronic and Information Engineering,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China University of Technology,School of Electronic and Information Engineering,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100390907","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":["South China University of Technology,School of Electronic and Information Engineering,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China University of Technology,School of Electronic and Information Engineering,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":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.7300000190734863,"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":0.7300000190734863,"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":0.03849999979138374,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.0203000009059906,"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/logit","display_name":"Logit","score":0.5917999744415283},{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.5321000218391418},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.5260999798774719},{"id":"https://openalex.org/keywords/mixed-logit","display_name":"Mixed logit","score":0.4684000015258789},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4456999897956848},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.43849998712539673}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7621999979019165},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.5917999744415283},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.5321000218391418},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.5260999798774719},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4763000011444092},{"id":"https://openalex.org/C95057490","wikidata":"https://www.wikidata.org/wiki/Q6883984","display_name":"Mixed logit","level":3,"score":0.4684000015258789},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4487999975681305},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4456999897956848},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.43849998712539673},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38190001249313354},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/pimrc62392.2025.11274983","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc62392.2025.11274983","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 36th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W2194775991","https://openalex.org/W2963209930","https://openalex.org/W3009048827","https://openalex.org/W3018102029","https://openalex.org/W3021654819","https://openalex.org/W3176474016","https://openalex.org/W4285603347","https://openalex.org/W4366352743","https://openalex.org/W4388145809","https://openalex.org/W4389371065","https://openalex.org/W4393159708","https://openalex.org/W4396240126","https://openalex.org/W4400910543","https://openalex.org/W4402703097","https://openalex.org/W4402754207","https://openalex.org/W4404563172","https://openalex.org/W4409153012","https://openalex.org/W4409363285"],"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":[11,18,61,64,79,93,105,108,113],"models":[12,57,87],"are":[13,58,88],"split":[14],"and":[15,20,25,75,85],"trained":[16],"on":[17],"server":[19],"clients.":[21,102],"However,":[22],"data":[23],"heterogeneity":[24],"partial":[26],"client":[27],"participation":[28],"result":[29],"in":[30,52,78],"label":[31,70,94],"distribution":[32,71,95],"skew,":[33],"which":[34,53],"severely":[35],"degrades":[36],"performance.":[38],"To":[39],"address":[40],"this":[41],"issue,":[42],"we":[43],"propose":[44],"Concatenated":[45],"Activations":[46],"Enabled":[47],"SFL":[48],"with":[49,92,112],"Logit":[50],"Adjustments,":[51],"activations":[54],"from":[55],"client-side":[56,86],"concatenated":[59],"as":[60],"input":[62],"of":[63,82,100,107],"server-side":[65,84],"model":[66],"to":[67,90],"centrally":[68],"adjust":[69],"across":[72,97],"different":[73,98],"clients,":[74],"logit":[76],"adjustments":[77],"loss":[80],"functions":[81],"both":[83],"performed":[89],"deal":[91],"variation":[96],"subsets":[99],"participating":[101],"Experiments":[103],"demonstrate":[104],"superiority":[106],"proposed":[109],"method":[110],"compared":[111],"traditional":[114],"schemes.":[115]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-12-12T00:00:00"}