{"id":"https://openalex.org/W7137996588","doi":"https://doi.org/10.1609/aaai.v40i32.39912","title":"Federated CLIP for Resource-Efficient Heterogeneous Medical Image Classification","display_name":"Federated CLIP for Resource-Efficient Heterogeneous Medical Image Classification","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7137996588","doi":"https://doi.org/10.1609/aaai.v40i32.39912"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i32.39912","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i32.39912","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39912/43873","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39912/43873","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129707696","display_name":"Yihang Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164862","display_name":"Artificial Intelligence in Medicine (Canada)","ror":"https://ror.org/05p590m36","country_code":"CA","type":"company","lineage":["https://openalex.org/I4210164862"]},{"id":"https://openalex.org/I5343935","display_name":"Guilin University of Electronic Technology","ror":"https://ror.org/05arjae42","country_code":"CN","type":"education","lineage":["https://openalex.org/I5343935"]}],"countries":["CA","CN"],"is_corresponding":true,"raw_author_name":"Yihang Wu","raw_affiliation_strings":["AIPM, School of Artificial Intelligence, Guilin University of Electronic Technology"],"affiliations":[{"raw_affiliation_string":"AIPM, School of Artificial Intelligence, Guilin University of Electronic Technology","institution_ids":["https://openalex.org/I4210164862","https://openalex.org/I5343935"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024700033","display_name":"Ahmad Chaddad","orcid":"https://orcid.org/0000-0003-3402-9576"},"institutions":[{"id":"https://openalex.org/I4210164862","display_name":"Artificial Intelligence in Medicine (Canada)","ror":"https://ror.org/05p590m36","country_code":"CA","type":"company","lineage":["https://openalex.org/I4210164862"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ahmad Chaddad","raw_affiliation_strings":["AIPM, School of Artificial Intelligence, Guilin University of Electronic Technology\nThe Imaging, Vision and Artificial Intelligence Laboratory, \u00c9cole de Technologie Sup\u00e9rieure"],"affiliations":[{"raw_affiliation_string":"AIPM, School of Artificial Intelligence, Guilin University of Electronic Technology\nThe Imaging, Vision and Artificial Intelligence Laboratory, \u00c9cole de Technologie Sup\u00e9rieure","institution_ids":["https://openalex.org/I4210164862"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5129707696"],"corresponding_institution_ids":["https://openalex.org/I4210164862","https://openalex.org/I5343935"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2114561,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"32","first_page":"26992","last_page":"27000"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.7513999938964844,"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":0.7513999938964844,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.031099999323487282,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.014100000262260437,"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/federated-learning","display_name":"Federated learning","score":0.4869000017642975},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.46459999680519104},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.4115000069141388},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4097999930381775},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.4034999907016754},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.39629998803138733},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.39629998803138733},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.36039999127388},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.35030001401901245},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3257000148296356}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8446999788284302},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.566100001335144},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5162000060081482},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.4869000017642975},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.46459999680519104},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4097999930381775},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4034999907016754},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.39629998803138733},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.39629998803138733},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3912000060081482},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.36039999127388},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3257000148296356},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3221000134944916},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.2937000095844269},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.29319998621940613},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.28540000319480896},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C154874363","wikidata":"https://www.wikidata.org/wiki/Q3518464","display_name":"Medical classification","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C39891107","wikidata":"https://www.wikidata.org/wiki/Q5767098","display_name":"Hinge loss","level":3,"score":0.25429999828338623},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i32.39912","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i32.39912","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39912/43873","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i32.39912","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i32.39912","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39912/43873","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7137996588.pdf","grobid_xml":"https://content.openalex.org/works/W7137996588.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Despite":[0],"the":[1,56,102,107,112,131,160],"remarkable":[2],"performance":[3,183],"of":[4,23,58],"deep":[5],"models":[6,66],"in":[7,21],"medical":[8,84,175],"imaging,":[9],"they":[10],"still":[11],"require":[12],"source":[13],"data":[14,50],"for":[15,83,167],"training,":[16],"which":[17],"limits":[18],"their":[19],"potential":[20],"light":[22],"privacy":[24],"concerns.":[25],"Federated":[26],"learning":[27,32,148],"(FL),":[28],"as":[29,96,123],"a":[30,36,46,74,90,97,118,124],"decentralized":[31],"framework":[33],"that":[34,178],"trains":[35],"shared":[37],"model":[38,156,180],"with":[39,194],"multiple":[40],"hospitals":[41],"(a.k.a.,":[42],"FL":[43,59,81],"clients),":[44],"provides":[45,181],"feasible":[47,182],"solution.":[48],"However,":[49],"heterogeneity":[51],"and":[52,151],"resource":[53,196],"costs":[54],"hinder":[55],"deployment":[57],"models,":[60],"especially":[61],"when":[62],"using":[63,164],"vision":[64],"language":[65],"(VLM).":[67],"To":[68],"address":[69],"these":[70],"challenges,":[71],"we":[72,88,116,135,154],"propose":[73,117],"novel":[75],"contrastive":[76],"language-image":[77],"pre-training":[78],"(CLIP)":[79],"based":[80],"approach":[82],"image":[85],"classification.":[86,168],"Specifically,":[87],"introduce":[89],"masked":[91,119],"feature":[92],"adaptation":[93],"module":[94,99],"(FAM)":[95],"communication":[98,103],"to":[100,110,128,130,145,158,188],"reduce":[101,111],"load":[104],"while":[105,163],"freezing":[106],"CLIP":[108],"encoders":[109],"computational":[113],"overhead.":[114],"Furthermore,":[115],"multi-layer":[120],"perceptron":[121],"(MLP)":[122],"private":[125],"local":[126],"classifier":[127],"adapt":[129],"client":[132],"tasks.":[133],"Moreover,":[134],"design":[136],"an":[137],"adaptive":[138],"Kullback-Leibler":[139],"(KL)":[140],"divergence-based":[141],"distillation":[142],"regularization":[143],"method":[144],"enable":[146],"mutual":[147],"between":[149],"FAM":[150,161],"MLP.":[152],"Finally,":[153],"incorporate":[155],"compression":[157],"transmit":[159],"parameters":[162],"ensemble":[165],"predictions":[166],"Extensive":[169],"experiments":[170],"on":[171,192],"four":[172],"publicly":[173],"available":[174],"datasets":[176],"demonstrate":[177],"our":[179],"(e.g.,":[184,198],"8%":[185],"higher":[186],"compared":[187],"second":[189],"best":[190],"baseline":[191],"ISIC2019)":[193],"reasonable":[195],"cost":[197],"120":[199],"times":[200],"faster":[201],"than":[202],"FedAVG).":[203]},"counts_by_year":[],"updated_date":"2026-03-20T20:47:17.329874","created_date":"2026-03-18T00:00:00"}