{"id":"https://openalex.org/W7160311216","doi":"https://doi.org/10.1109/wacv61042.2026.00400","title":"FedSCAl: Leveraging Server and Client Alignment for Unsupervised Federated Source-Free Domain Adaptation","display_name":"FedSCAl: Leveraging Server and Client Alignment for Unsupervised Federated Source-Free Domain Adaptation","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7160311216","doi":"https://doi.org/10.1109/wacv61042.2026.00400"},"language":null,"primary_location":{"id":"doi:10.1109/wacv61042.2026.00400","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00400","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","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/A5016822769","display_name":"M. Yashwanth","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M. Yashwanth","raw_affiliation_strings":["Indian Institute of Science,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science,Bengaluru,India","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007235367","display_name":"Sampath Koti","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sampath Koti","raw_affiliation_strings":["Indian Institute of Science,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science,Bengaluru,India","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135311770","display_name":"Arunabh Singh","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Arunabh Singh","raw_affiliation_strings":["Indian Institute of Science,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science,Bengaluru,India","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072834529","display_name":"Shyam Marjit","orcid":"https://orcid.org/0000-0002-7385-4432"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shyam Marjit","raw_affiliation_strings":["Indian Institute of Science,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science,Bengaluru,India","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5135388773","display_name":"Anirban Chakraborty","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anirban Chakraborty","raw_affiliation_strings":["Indian Institute of Science,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science,Bengaluru,India","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I59270414"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.45943717,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4108","last_page":"4117"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6973000168800354,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6973000168800354,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.02370000071823597,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.016699999570846558,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/domain","display_name":"Domain (mathematical analysis)","score":0.5627999901771545},{"id":"https://openalex.org/keywords/client\u2013server-model","display_name":"Client\u2013server model","score":0.4004000127315521},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.39910000562667847},{"id":"https://openalex.org/keywords/server","display_name":"Server","score":0.33730000257492065},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.2919999957084656}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.73089998960495},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5627999901771545},{"id":"https://openalex.org/C152880691","wikidata":"https://www.wikidata.org/wiki/Q146813","display_name":"Client\u2013server model","level":3,"score":0.4004000127315521},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.39910000562667847},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3050000071525574},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.30300000309944153},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3019999861717224},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.2912999987602234},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2572999894618988}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv61042.2026.00400","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00400","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","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":30,"referenced_works":["https://openalex.org/W2022852240","https://openalex.org/W2104094955","https://openalex.org/W2149466042","https://openalex.org/W2165501671","https://openalex.org/W2627183927","https://openalex.org/W2783522756","https://openalex.org/W2991236681","https://openalex.org/W3012501605","https://openalex.org/W3096572172","https://openalex.org/W3100779497","https://openalex.org/W3104631511","https://openalex.org/W3108946571","https://openalex.org/W3133814152","https://openalex.org/W3155912831","https://openalex.org/W3182158470","https://openalex.org/W4213177592","https://openalex.org/W4226461837","https://openalex.org/W4281753687","https://openalex.org/W4283798023","https://openalex.org/W4312440658","https://openalex.org/W4312517097","https://openalex.org/W4312869277","https://openalex.org/W4319300101","https://openalex.org/W4381252687","https://openalex.org/W4386076561","https://openalex.org/W4390872376","https://openalex.org/W4394625646","https://openalex.org/W4409262779","https://openalex.org/W7133230604","https://openalex.org/W7133242886"],"related_works":[],"abstract_inverted_index":{"We":[0,127],"address":[1,60],"the":[2,23,34,39,43,56,61,65,70,91,121,132,139,145,165],"Federated":[3],"source-Free":[4],"Domain":[5,72],"Adaptation":[6,73],"(FFreeDA)":[7],"problem,":[8],"with":[9,14,79],"clients":[10],"holding":[11],"unlabeled":[12],"data":[13,87],"significant":[15],"inter-client":[16],"domain":[17,49,93],"gaps.":[18],"The":[19],"FFreeDA":[20,66,166],"setup":[21,35,167],"constrains":[22],"FL":[24,77,106,162],"frameworks":[25],"to":[26,38,55,76,85,115,143],"employ":[27],"only":[28],"a":[29,52],"pre-trained":[30],"server":[31,124],"model":[32],"as":[33,138],"restricts":[36],"access":[37],"source":[40,48],"dataset":[41,50],"during":[42],"training":[44],"rounds.":[45],"Often,":[46],"this":[47,100],"has":[51],"distinct":[53],"distribution":[54],"clients\u2019":[57,122,133],"domains.":[58],"To":[59],"challenges":[62],"posed":[63],"by":[64,90,119],"setup,":[67],"adaptation":[68],"of":[69],"Source-Free":[71],"(SFDA)":[74],"methods":[75,163],"struggles":[78],"client-drift":[80],"in":[81,96,131,164],"real-world":[82],"scenarios":[83],"due":[84],"extreme":[86],"heterogeneity":[88],"caused":[89],"aforementioned":[92],"gaps,":[94],"resulting":[95],"unreliable":[97],"pseudo-labels.":[98],"In":[99],"paper,":[101],"we":[102,148],"introduce":[103],"FedSCAl,":[104],"an":[105,129],"framework":[107],"leveraging":[108],"our":[109],"proposed":[110],"Server-Client":[111],"Alignment":[112],"(SCAl)":[113],"mechanism":[114,141],"regularize":[116],"client":[117],"updates":[118],"aligning":[120],"and":[123],"model\u2019s":[125],"predictions.":[126],"observe":[128],"improvement":[130],"pseudo-labeling":[134],"accuracy":[135],"post":[136],"alignment,":[137],"SCAl":[140],"helps":[142],"mitigate":[144],"client-drift.":[146],"Further,":[147],"present":[149],"extensive":[150],"experiments":[151],"on":[152],"benchmark":[153],"vision":[154],"datasets":[155],"showcasing":[156],"how":[157],"FedSCAl":[158],"consistently":[159],"outperforms":[160],"state-of-the-art":[161],"for":[168],"classification":[169],"tasks.":[170]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-05-06T00:00:00"}