{"id":"https://openalex.org/W4410772636","doi":"https://doi.org/10.1145/3727353.3727385","title":"FMLM: A federated meta-learning method for learning multimodal data features","display_name":"FMLM: A federated meta-learning method for learning multimodal data features","publication_year":2025,"publication_date":"2025-01-10","ids":{"openalex":"https://openalex.org/W4410772636","doi":"https://doi.org/10.1145/3727353.3727385"},"language":"en","primary_location":{"id":"doi:10.1145/3727353.3727385","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727353.3727385","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727385","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727385","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011298645","display_name":"Meng Yue","orcid":"https://orcid.org/0000-0002-1473-3729"},"institutions":[{"id":"https://openalex.org/I28813325","display_name":"Civil Aviation University of China","ror":"https://ror.org/03je71k37","country_code":"CN","type":"education","lineage":["https://openalex.org/I28813325"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Yue","raw_affiliation_strings":["College of Safety Science and Engineering, Civil Aviation University of China, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-1473-3729","affiliations":[{"raw_affiliation_string":"College of Safety Science and Engineering, Civil Aviation University of China, Tianjin, China","institution_ids":["https://openalex.org/I28813325"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041262691","display_name":"Weiyun Li","orcid":"https://orcid.org/0000-0002-9446-9242"},"institutions":[{"id":"https://openalex.org/I28813325","display_name":"Civil Aviation University of China","ror":"https://ror.org/03je71k37","country_code":"CN","type":"education","lineage":["https://openalex.org/I28813325"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiyun Li","raw_affiliation_strings":["College of Safety Science and Engineering, Civil Aviation University of China, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-9446-9242","affiliations":[{"raw_affiliation_string":"College of Safety Science and Engineering, Civil Aviation University of China, Tianjin, China","institution_ids":["https://openalex.org/I28813325"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I28813325"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07261795,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"178","last_page":"183"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9822999835014343,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9822999835014343,"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":0.9581999778747559,"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/T11309","display_name":"Music and Audio Processing","score":0.9559999704360962,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.8232482671737671},{"id":"https://openalex.org/keywords/meta-learning","display_name":"Meta learning (computer science)","score":0.647508978843689},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5136932730674744},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4165154695510864},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0746212899684906},{"id":"https://openalex.org/keywords/systems-engineering","display_name":"Systems engineering","score":0.04951849579811096}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8232482671737671},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.647508978843689},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5136932730674744},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4165154695510864},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0746212899684906},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.04951849579811096},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3727353.3727385","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727353.3727385","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727385","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3727353.3727385","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727353.3727385","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727385","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1890901198","display_name":null,"funder_award_id":"62172418","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G272075324","display_name":null,"funder_award_id":"21JCZDJC00830","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"},{"id":"https://openalex.org/G2827442430","display_name":null,"funder_award_id":"62172418","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323993","display_name":"Natural Science Foundation of Tianjin City","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410772636.pdf","grobid_xml":"https://content.openalex.org/works/W4410772636.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W2541884796","https://openalex.org/W2604763608","https://openalex.org/W2963819344","https://openalex.org/W2990138404","https://openalex.org/W3014140399","https://openalex.org/W3099314130","https://openalex.org/W3136896197","https://openalex.org/W3138059307","https://openalex.org/W3168997536","https://openalex.org/W4285248692","https://openalex.org/W4292737460","https://openalex.org/W4307317167","https://openalex.org/W4308243215","https://openalex.org/W4361983774","https://openalex.org/W4362603537","https://openalex.org/W4365790369","https://openalex.org/W4377713808","https://openalex.org/W4381850046","https://openalex.org/W4385292108","https://openalex.org/W4385567856","https://openalex.org/W4387987076","https://openalex.org/W4388469770","https://openalex.org/W4389352393","https://openalex.org/W4390690068","https://openalex.org/W4393256310","https://openalex.org/W4399154557","https://openalex.org/W4399801293","https://openalex.org/W4401596672","https://openalex.org/W4401836692","https://openalex.org/W4404788222","https://openalex.org/W4404809929","https://openalex.org/W4405175807","https://openalex.org/W6790690058"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Federated":[0],"meta-learning,":[1,110],"which":[2,172],"possesses":[3],"the":[4,45,52,56,64,76,105,111,123,133,144,168,175,206],"ability":[5,65],"of":[6,23,47,51,58,66,78,114,150,155,201,208],"fast":[7],"adaptive":[8],"learning":[9,54],"and":[10,36,70,122,132,160,180,203],"privacy":[11,181],"preservation,":[12],"has":[13,26,39],"gained":[14],"great":[15],"momentum":[16],"in":[17,44,55,82,108,129,147,166,204],"personalized":[18,156],"federated":[19,37,94,151],"learning.":[20],"The":[21],"development":[22],"IoT":[24],"technology":[25],"led":[27],"to":[28,74,98,196],"more":[29],"demand":[30],"for":[31,138],"sharing":[32,162],"different":[33],"modal":[34,83],"data,":[35],"meta-learning":[38,95],"a":[40,92,153,185,198],"broad":[41],"application":[42],"prospect":[43],"field":[46],"multimodal":[48,59,67,101,125],"data.":[49],"Many":[50],"existing":[53],"context":[57],"data":[60,68,80,84,102],"focus":[61],"on":[62,143],"improving":[63],"fusion,":[69],"it":[71],"is":[72,117,127,158,163,194,212],"easy":[73],"ignore":[75],"advantages":[77],"unimodal":[79,115,139],"features":[81],"feature":[85,169,177],"extraction.":[86],"In":[87,184],"this":[88],"paper,":[89],"we":[90],"propose":[91],"new":[93],"approach":[96],"FMLM":[97],"efficiently":[99],"extract":[100],"features.":[103],"On":[104],"one":[106],"hand,":[107,146],"local":[109],"optimal":[112],"representation":[113],"modalities":[116],"obtained":[118],"by":[119],"internal":[120],"updating,":[121,131],"fused":[124],"model":[126,140],"updated":[128],"external":[130],"update":[134],"results":[135],"are":[136],"used":[137],"parameter":[141,161],"updating;":[142],"other":[145],"multi-party":[148],"aggregation":[149,157],"learning,":[152],"kind":[154],"considered,":[159],"carried":[164],"out":[165],"only":[167],"extraction":[170,178],"layer,":[171],"further":[173],"improves":[174],"model's":[176],"effect":[179],"preservation":[182],"Performance.":[183],"publicly":[186],"available":[187],"human":[188],"action":[189],"recognition":[190],"dataset,":[191],"our":[192,209],"method":[193,211],"able":[195],"achieve":[197],"privacy-preserving":[199],"accuracy":[200],"69.92%,":[202],"addition,":[205],"effectiveness":[207],"proposed":[210],"verified":[213],"through":[214],"well-established":[215],"comparative":[216],"experiments.":[217]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}