{"id":"https://openalex.org/W4413361243","doi":"https://doi.org/10.1109/icde65448.2025.00166","title":"pFSSL-D: Generalization Meets Personalization in Dual-Phase Federated Semi-Supervised Learning","display_name":"pFSSL-D: Generalization Meets Personalization in Dual-Phase Federated Semi-Supervised Learning","publication_year":2025,"publication_date":"2025-05-19","ids":{"openalex":"https://openalex.org/W4413361243","doi":"https://doi.org/10.1109/icde65448.2025.00166"},"language":"en","primary_location":{"id":"doi:10.1109/icde65448.2025.00166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde65448.2025.00166","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 41st International Conference on Data Engineering (ICDE)","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/A5100377773","display_name":"Yuting Li","orcid":"https://orcid.org/0000-0003-0077-1684"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuting Li","raw_affiliation_strings":["Institute of Artificial Intelligence and Future Networks, Beijing Normal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence and Future Networks, Beijing Normal University","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100738585","display_name":"Wenhua Wang","orcid":"https://orcid.org/0000-0002-7682-0860"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhua Wang","raw_affiliation_strings":["Institute of Artificial Intelligence and Future Networks, Beijing Normal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence and Future Networks, Beijing Normal University","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006546107","display_name":"Tian Wang","orcid":"https://orcid.org/0000-0003-4819-621X"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tian Wang","raw_affiliation_strings":["Institute of Artificial Intelligence and Future Networks, Beijing Normal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence and Future Networks, Beijing Normal University","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I25254941"],"apc_list":null,"apc_paid":null,"fwci":1.403,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.82453124,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2188","last_page":"2200"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9325000047683716,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9325000047683716,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9114000201225281,"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/personalization","display_name":"Personalization","score":0.8122885227203369},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7770494818687439},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.7432360649108887},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.7066020369529724},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3831322491168976},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.3543564975261688},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.3123648762702942},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09766274690628052}],"concepts":[{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.8122885227203369},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7770494818687439},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.7432360649108887},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7066020369529724},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3831322491168976},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3543564975261688},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.3123648762702942},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09766274690628052},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icde65448.2025.00166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde65448.2025.00166","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 41st International Conference on Data Engineering (ICDE)","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":26,"referenced_works":["https://openalex.org/W3035682985","https://openalex.org/W3042609801","https://openalex.org/W3093331990","https://openalex.org/W3104631511","https://openalex.org/W4312462223","https://openalex.org/W4319997089","https://openalex.org/W4382463479","https://openalex.org/W4385574960","https://openalex.org/W4386075641","https://openalex.org/W4387969057","https://openalex.org/W4389960113","https://openalex.org/W4390872538","https://openalex.org/W4393154273","https://openalex.org/W4393156233","https://openalex.org/W4393160263","https://openalex.org/W4396712810","https://openalex.org/W4398199732","https://openalex.org/W4400909485","https://openalex.org/W4400909669","https://openalex.org/W4400909762","https://openalex.org/W4400909769","https://openalex.org/W4400909843","https://openalex.org/W4400909961","https://openalex.org/W4404238206","https://openalex.org/W4404787909","https://openalex.org/W4407213320"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2109940557","https://openalex.org/W2466832359","https://openalex.org/W4391210591","https://openalex.org/W1582019636","https://openalex.org/W1499005795","https://openalex.org/W3162204513","https://openalex.org/W3172493050","https://openalex.org/W4385420271","https://openalex.org/W4312192618"],"abstract_inverted_index":{"Federated":[0],"Semi-Supervised":[1],"Learning":[2],"(FSSL)":[3],"offers":[4],"a":[5,55,85,102],"distributed":[6],"learning":[7,76],"paradigm":[8],"that":[9,146],"addresses":[10],"the":[11,71,97],"critical":[12],"issue":[13],"of":[14,155],"label":[15],"scarcity":[16],"while":[17,92],"preserving":[18],"client":[19],"privacy.":[20],"However,":[21],"current":[22],"FSSL":[23,151],"methods":[24,152],"are":[25],"often":[26],"hindered":[27],"by":[28],"an":[29],"over-reliance":[30],"on":[31,142],"labeled":[32],"data":[33,46],"for":[34,67,123],"initialization,":[35],"high":[36],"communication":[37,94],"overhead,":[38],"and":[39,59,87,116,159],"suboptimal":[40],"global":[41,130],"model":[42,91,112],"performance":[43],"in":[44,137,153],"heterogeneous":[45,138],"settings.":[47],"To":[48],"overcome":[49],"these":[50],"limitations,":[51],"we":[52,100],"propose":[53],"pFSSL-D,":[54],"novel":[56],"Dual-Phase":[57],"Generalization":[58],"Personalization":[60],"Pipeline":[61],"designed":[62],"to":[63,82],"generate":[64],"several":[65],"models":[66],"unlabeled":[68],"clients.":[69],"In":[70,96],"first":[72],"phase,":[73,99],"decentralized":[74],"contrastive":[75],"with":[77,107,132],"feature":[78,89],"alignment":[79],"is":[80],"proposed":[81],"efficiently":[83],"pre-train":[84],"robust":[86],"generalizable":[88],"extraction":[90],"minimizing":[93],"overhead.":[95,161],"personalization":[98],"introduce":[101],"parameter-granularity":[103],"federated":[104],"fine-tuning":[105],"approach":[106],"semantic":[108],"consistency,":[109],"which":[110],"decouples":[111],"parameters":[113],"into":[114],"general":[115],"personalized":[117],"components,":[118],"providing":[119],"specialized":[120],"update":[121],"strategies":[122],"each":[124],"component.":[125],"This":[126],"method":[127],"effectively":[128],"balances":[129],"generalization":[131],"client-specific":[133],"adaptation,":[134],"ensuring":[135],"robustness":[136],"environments.":[139],"Extensive":[140],"evaluations":[141],"benchmark":[143],"datasets":[144],"show":[145],"pFSSL-D":[147],"consistently":[148],"outperforms":[149],"state-of-the-art":[150],"terms":[154],"accuracy,":[156],"convergence":[157],"speed,":[158],"resource":[160]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}