{"id":"https://openalex.org/W7104558879","doi":"https://doi.org/10.1109/tbdata.2025.3630997","title":"Toward Federated Learning Against Noisy Clients via CLIP-Guided Prototypes","display_name":"Toward Federated Learning Against Noisy Clients via CLIP-Guided Prototypes","publication_year":2025,"publication_date":"2025-11-10","ids":{"openalex":"https://openalex.org/W7104558879","doi":"https://doi.org/10.1109/tbdata.2025.3630997"},"language":null,"primary_location":{"id":"doi:10.1109/tbdata.2025.3630997","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2025.3630997","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Big Data","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":null,"display_name":"Zhou Tan","orcid":"https://orcid.org/0009-0000-9030-6406"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhou Tan","raw_affiliation_strings":["College of Computer and Data Science, Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0009-0000-9030-6406","affiliations":[{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":null,"display_name":"De Li","orcid":"https://orcid.org/0009-0008-0202-4723"},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"De Li","raw_affiliation_strings":["School of Computer Science and Engineering, Guangxi Normal University, Guilin, China"],"raw_orcid":"https://orcid.org/0009-0008-0202-4723","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Guangxi Normal University, Guilin, China","institution_ids":["https://openalex.org/I29739308"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jianping Cai","orcid":null},"institutions":[{"id":"https://openalex.org/I6469544","display_name":"City University of Macau","ror":"https://ror.org/04gpd4q15","country_code":"MO","type":"education","lineage":["https://openalex.org/I6469544"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Jianping Cai","raw_affiliation_strings":["Faculty of Data Science, City University of Macau, Macau, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Data Science, City University of Macau, Macau, China","institution_ids":["https://openalex.org/I6469544"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yirui Huang","orcid":"https://orcid.org/0000-0001-5425-6333"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yirui Huang","raw_affiliation_strings":["College of Computer and Data Science, Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-5425-6333","affiliations":[{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chunpei Li","orcid":"https://orcid.org/0000-0002-0250-5374"},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunpei Li","raw_affiliation_strings":["School of Computer Science and Engineering, Guangxi Normal University, Guilin, China"],"raw_orcid":"https://orcid.org/0000-0002-0250-5374","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Guangxi Normal University, Guilin, China","institution_ids":["https://openalex.org/I29739308"]}]},{"author_position":"last","author":{"id":null,"display_name":"Ximeng Liu","orcid":"https://orcid.org/0000-0002-4238-3295"},"institutions":[{"id":"https://openalex.org/I4210145005","display_name":"State Key Laboratory of Vehicle NVH and Safety Technology","ror":"https://ror.org/04e6h1p91","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210145005"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ximeng Liu","raw_affiliation_strings":["State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing Changan Automobile Company, Ltd., Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0002-4238-3295","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing Changan Automobile Company, Ltd., Chongqing, China","institution_ids":["https://openalex.org/I4210145005"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.74131965,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":"2","first_page":"509","last_page":"524"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9369999766349792,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9369999766349792,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.010499999858438969,"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":0.006800000090152025,"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/federated-learning","display_name":"Federated learning","score":0.675599992275238},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5956000089645386},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.5819000005722046},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5464000105857849},{"id":"https://openalex.org/keywords/adapter","display_name":"Adapter (computing)","score":0.5278000235557556},{"id":"https://openalex.org/keywords/independent-and-identically-distributed-random-variables","display_name":"Independent and identically distributed random variables","score":0.49300000071525574},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4580000042915344},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44600000977516174}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.9125000238418579},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.675599992275238},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5956000089645386},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.5819000005722046},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5464000105857849},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.5278000235557556},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.49300000071525574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48969998955726624},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4580000042915344},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44600000977516174},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.43059998750686646},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42419999837875366},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38659998774528503},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.35850000381469727},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3434999883174896},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.30649998784065247},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C2780909371","wikidata":"https://www.wikidata.org/wiki/Q4801092","display_name":"Artificial noise","level":4,"score":0.2800999879837036},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2578999996185303},{"id":"https://openalex.org/C187191949","wikidata":"https://www.wikidata.org/wiki/Q1138496","display_name":"Profiling (computer programming)","level":2,"score":0.2563000023365021}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tbdata.2025.3630997","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2025.3630997","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Big Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8230365729","display_name":null,"funder_award_id":"62402111","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1921293667","https://openalex.org/W2112796928","https://openalex.org/W2594788739","https://openalex.org/W2896457183","https://openalex.org/W2963697299","https://openalex.org/W2964274690","https://openalex.org/W2985580374","https://openalex.org/W3200318570","https://openalex.org/W3215126987","https://openalex.org/W4213446860","https://openalex.org/W4283790910","https://openalex.org/W4286611322","https://openalex.org/W4287947470","https://openalex.org/W4293791267","https://openalex.org/W4312403682","https://openalex.org/W4312480274","https://openalex.org/W4312558481","https://openalex.org/W4382463479","https://openalex.org/W4385245566","https://openalex.org/W4385767857","https://openalex.org/W4386790226","https://openalex.org/W4389476336","https://openalex.org/W4390788106","https://openalex.org/W4390873546","https://openalex.org/W4392901776","https://openalex.org/W4393147780","https://openalex.org/W4393149358","https://openalex.org/W4393156437","https://openalex.org/W4393156671","https://openalex.org/W4393157174","https://openalex.org/W4393160079","https://openalex.org/W4397000111","https://openalex.org/W4400909843","https://openalex.org/W4402754207"],"related_works":[],"abstract_inverted_index":{"Federated":[0],"Noisy":[1],"Labels":[2],"Learning":[3],"(FNLL)":[4],"allows":[5],"global":[6,93],"model":[7,110],"to":[8,53,75,87,102],"be":[9],"jointly":[10],"trained":[11],"on":[12,126],"multiple":[13],"clients":[14,45],"with":[15,156],"varying":[16],"degrees":[17],"of":[18,42,57,120,178],"noisy":[19,34,49,83,149,181],"labels":[20,43,84],"while":[21],"preserving":[22],"privacy,":[23],"and":[24,33,50,132,136,154,167,171],"despite":[25],"recent":[26],"research":[27],"advances,":[28],"distinguishing":[29],"between":[30],"client":[31],"clean":[32],"samples":[35,150],"is":[36,46],"still":[37],"tricky":[38],"since":[39],"the":[40,54,72,92,118,157,162,176],"distribution":[41],"among":[44],"always":[47],"both":[48,130],"class-imbalanced,":[51],"leading":[52],"poor":[55],"performance":[56,90,173],"existing":[58],"FNLL":[59,160],"methods.":[60],"To":[61],"address":[62],"this":[63],"problem,":[64],"we":[65,95],"propose":[66],"a":[67],"novel":[68],"framework":[69,74],"called":[70],"FedPN,":[71],"first":[73],"utilize":[76],"Contrastive":[77],"Language-Image":[78],"Pre-training":[79],"(CLIP)":[80],"for":[81,91,108],"federated":[82],"tasks.":[85],"Then,":[86],"achieve":[88],"higher":[89],"model,":[94],"introduce":[96],"an":[97],"attention":[98],"based":[99],"Prototype":[100],"Adapter":[101],"identify":[103],"more":[104],"plausible":[105],"local":[106,109],"data":[107,138],"training,":[111],"further":[112],"improving":[113],"training":[114],"stability.":[115],"We":[116],"validate":[117],"effectiveness":[119],"FedPN":[121,145,163],"by":[122],"conducting":[123],"extensive":[124],"experiments":[125],"benchmark":[127],"datasets":[128],"under":[129],"Independently":[131],"Identically":[133],"Distributed":[134],"(IID)":[135],"Non-IID":[137],"partitions.":[139],"The":[140],"experimental":[141],"results":[142],"show":[143],"that":[144],"can":[146],"effectively":[147],"filter":[148],"from":[151],"different":[152],"clients,":[153],"compared":[155],"state":[158],"of-the-art":[159],"method,":[161],"achieves":[164],"at":[165,168],"most":[166],"least":[169],"8.39%":[170],"0.88%":[172],"improvement":[174],"in":[175],"case":[177],"highly":[179],"heterogeneous":[180],"labels.":[182]},"counts_by_year":[],"updated_date":"2026-07-16T05:54:29.318815","created_date":"2025-11-10T00:00:00"}