{"id":"https://openalex.org/W4403791731","doi":"https://doi.org/10.1145/3664647.3681490","title":"FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models","display_name":"FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403791731","doi":"https://doi.org/10.1145/3664647.3681490"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3681490","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681490","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-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":"https://openalex.org/A5059977860","display_name":"Mingzhao Yang","orcid":"https://orcid.org/0009-0005-3140-3691"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingzhao Yang","raw_affiliation_strings":["School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0005-3140-3691","affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005344729","display_name":"Shangchao Su","orcid":"https://orcid.org/0000-0001-9925-1114"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shangchao Su","raw_affiliation_strings":["School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-9925-1114","affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100365239","display_name":"Bin Li","orcid":"https://orcid.org/0000-0002-9633-0033"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Li","raw_affiliation_strings":["School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-9633-0033","affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003418019","display_name":"Xiangyang Xue","orcid":"https://orcid.org/0000-0002-4897-9209"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyang Xue","raw_affiliation_strings":["School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-4897-9209","affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8328,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.87846281,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"6666","last_page":"6675"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9936000108718872,"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":0.9936000108718872,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9524999856948853,"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/T10028","display_name":"Topic Modeling","score":0.9463000297546387,"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/computer-science","display_name":"Computer science","score":0.7372346520423889},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.7077748775482178},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.6790032386779785},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.08707192540168762},{"id":"https://openalex.org/keywords/thermodynamics","display_name":"Thermodynamics","score":0.059285879135131836},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.059037476778030396}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7372346520423889},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.7077748775482178},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.6790032386779785},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.08707192540168762},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.059285879135131836},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.059037476778030396},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3681490","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681490","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2981720610","https://openalex.org/W3035453001","https://openalex.org/W3036167779","https://openalex.org/W3212516020","https://openalex.org/W3217030260","https://openalex.org/W4226480431","https://openalex.org/W4281485151","https://openalex.org/W4288083516","https://openalex.org/W4312740349","https://openalex.org/W4312824283","https://openalex.org/W4312933868","https://openalex.org/W4367056838","https://openalex.org/W4386072096","https://openalex.org/W4390872376","https://openalex.org/W4393160127","https://openalex.org/W6779823529","https://openalex.org/W6810565495"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2074502265","https://openalex.org/W4214877189","https://openalex.org/W2773965352","https://openalex.org/W2381179799","https://openalex.org/W2980279061","https://openalex.org/W2334685461","https://openalex.org/W2366718574"],"abstract_inverted_index":{"In":[0,69],"recent":[1],"years,":[2],"the":[3,20,23,30,46,51,61,89,104,108,112,115,119,127,132,142,145,147,155,164,170,173,192,197,218,233],"attention":[4],"towards":[5],"One-Shot":[6,77],"Federated":[7,78],"Learning":[8,79],"(OSFL)":[9],"has":[10],"been":[11],"driven":[12],"by":[13],"its":[14],"capacity":[15],"to":[16,44,50,110,118,130,141,153],"minimize":[17],"communication.":[18],"With":[19],"development":[21],"of":[22,87,96,114,134,166,172,194,201,236],"diffusion":[24],"model":[25,35,220],"(DM),":[26],"several":[27],"methods":[28,227],"employ":[29],"DM":[31,90,156],"for":[32],"OSFL,":[33],"utilizing":[34,88],"parameters,":[36],"image":[37],"features,":[38],"or":[39,60,224],"textual":[40],"prompts":[41],"as":[42,107,151],"mediums":[43,55],"transfer":[45,111],"local":[47,101,124,195],"client":[48,128,135],"knowledge":[49,113],"server.":[52,120,143],"However,":[53],"these":[54],"often":[56],"require":[57],"public":[58],"datasets":[59,160,188,204],"uniform":[62],"feature":[63],"extractor,":[64],"significantly":[65],"limiting":[66],"their":[67],"practicality.":[68],"this":[70],"paper,":[71],"we":[72,122],"propose":[73],"FedDEO,":[74],"a":[75,84],"Description-Enhanced":[76],"Method":[80],"with":[81,163,205,211],"DMs,":[82],"offering":[83],"novel":[85],"exploration":[86],"in":[91,157,213],"OSFL.":[92],"The":[93],"core":[94],"idea":[95],"our":[97],"method":[98],"involves":[99],"training":[100,171,193],"descriptions":[102,125,148],"on":[103,126,184,229],"clients,":[105,168,231],"serving":[106],"medium":[109],"distributed":[116],"clients":[117],"Firstly,":[121],"train":[123],"data":[129],"capture":[131],"characteristics":[133],"distributions,":[136],"which":[137],"are":[138,149],"then":[139],"uploaded":[140],"On":[144],"server,":[146],"used":[150],"conditions":[152],"guide":[154],"generating":[158,202],"synthetic":[159,203],"that":[161,190],"comply":[162],"distributions":[165],"various":[167],"enabling":[169],"aggregated":[174,219],"model.":[175],"Theoretical":[176],"analyses":[177],"and":[178,181,208,215],"sufficient":[179],"quantitation":[180],"visualization":[182],"experiments":[183],"three":[185],"large-scale":[186],"real-world":[187],"demonstrate":[189],"through":[191],"descriptions,":[196],"server":[198],"is":[199],"capable":[200],"high":[206],"quality":[207],"diversity.":[209],"Consequently,":[210],"advantages":[212],"communication":[214],"privacy":[216],"protection,":[217],"outperforms":[221,232],"compared":[222],"FL":[223],"diffusion-based":[225],"OSFL":[226],"and,":[228],"some":[230],"performance":[234],"ceiling":[235],"centralized":[237],"training.":[238]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}