{"id":"https://openalex.org/W4415124582","doi":"https://doi.org/10.1109/icmlt65785.2025.11193423","title":"An Investigation of Parameter Efficient Federated Learning with Foundation Model","display_name":"An Investigation of Parameter Efficient Federated Learning with Foundation Model","publication_year":2025,"publication_date":"2025-05-23","ids":{"openalex":"https://openalex.org/W4415124582","doi":"https://doi.org/10.1109/icmlt65785.2025.11193423"},"language":"en","primary_location":{"id":"doi:10.1109/icmlt65785.2025.11193423","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlt65785.2025.11193423","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 10th International Conference on Machine Learning Technologies (ICMLT)","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/A5100729939","display_name":"Zeyuan Wang","orcid":"https://orcid.org/0000-0001-7211-9543"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeyuan Wang","raw_affiliation_strings":["National University of Defense Technology,Laboratory for Big Data and Decision,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,Laboratory for Big Data and Decision,Changsha,China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110163804","display_name":"Zhendu Li","orcid":null},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhendu Li","raw_affiliation_strings":["National University of Defense Technology,Laboratory for Big Data and Decision,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,Laboratory for Big Data and Decision,Changsha,China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079425523","display_name":"Yanming Guo","orcid":"https://orcid.org/0000-0001-9184-5313"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanming Guo","raw_affiliation_strings":["National University of Defense Technology,Laboratory for Big Data and Decision,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,Laboratory for Big Data and Decision,Changsha,China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101681259","display_name":"Jun Tang","orcid":"https://orcid.org/0000-0001-8925-2367"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Tang","raw_affiliation_strings":["National University of Defense Technology,Laboratory for Big Data and Decision,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,Laboratory for Big Data and Decision,Changsha,China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24570264,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"233","last_page":"239"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9994000196456909,"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":0.9994000196456909,"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/T10237","display_name":"Cryptography and Data Security","score":0.9358999729156494,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9333999752998352,"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":0.8723000288009644},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6018999814987183},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.5995000004768372},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4885999858379364},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.42800000309944153},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.38830000162124634},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"Distributed learning","score":0.3319000005722046},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.3276999890804291}],"concepts":[{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.8723000288009644},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7646999955177307},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6018999814987183},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.5995000004768372},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4885999858379364},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4636000096797943},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.42800000309944153},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42410001158714294},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.38830000162124634},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.38029998540878296},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C67712803","wikidata":"https://www.wikidata.org/wiki/Q7901853","display_name":"User modeling","level":3,"score":0.3163999915122986},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C10431821","wikidata":"https://www.wikidata.org/wiki/Q6510174","display_name":"Learning effect","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C158156997","wikidata":"https://www.wikidata.org/wiki/Q1416645","display_name":"Models of communication","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2678000032901764},{"id":"https://openalex.org/C2780609101","wikidata":"https://www.wikidata.org/wiki/Q17156588","display_name":"Resource management (computing)","level":2,"score":0.2578999996185303}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlt65785.2025.11193423","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlt65785.2025.11193423","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 10th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"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":20,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2930926105","https://openalex.org/W2963026768","https://openalex.org/W2980708516","https://openalex.org/W3021654819","https://openalex.org/W3098267758","https://openalex.org/W3156841666","https://openalex.org/W3176828726","https://openalex.org/W3185341429","https://openalex.org/W3198377975","https://openalex.org/W4226376129","https://openalex.org/W4295037363","https://openalex.org/W4312352414","https://openalex.org/W4385245566","https://openalex.org/W4385737398","https://openalex.org/W4386072282","https://openalex.org/W4386790226","https://openalex.org/W4390548144","https://openalex.org/W4394828356","https://openalex.org/W4403391203"],"related_works":[],"abstract_inverted_index":{"Federated":[0],"learning":[1,7,21],"is":[2,11],"a":[3,73,78],"sub-area":[4],"of":[5,18,28,36,65],"distributed":[6],"where":[8],"user":[9,29],"privacy":[10],"particularly":[12],"concerned.":[13],"However,":[14],"the":[15,25,107],"increasing":[16],"scale":[17],"modern":[19],"machine":[20],"models,":[22],"coupled":[23],"with":[24,61],"resource":[26],"constraints":[27],"devices,":[30],"poses":[31],"significant":[32],"challenges":[33],"in":[34,124],"terms":[35],"server-client":[37],"communication":[38,91],"efficiency":[39],"and":[40,58,93,105],"local":[41,87],"model":[42,55,88,95],"training.":[43],"Various":[44],"research":[45,123],"efforts":[46],"have":[47],"been":[48],"engaged":[49],"to":[50,83],"address":[51],"them,":[52],"such":[53],"as":[54],"compression,":[56],"pruning":[57],"parameter":[59],"distillation":[60],"usually":[62],"side":[63],"effects":[64],"performance":[66],"degradation.":[67],"In":[68],"this":[69],"paper,":[70],"we":[71],"propose":[72],"novel":[74],"approach":[75],"that":[76],"leverages":[77],"foundation":[79],"model,":[80],"specifically":[81],"CLIP,":[82],"simultaneously":[84],"achieve":[85],"efficient":[86],"training,":[89],"reduced":[90],"overhead,":[92],"superior":[94],"performance.":[96],"We":[97],"explore":[98],"various":[99],"parameter-efficient":[100,125],"designs":[101],"based":[102],"on":[103],"CLIP":[104],"identify":[106],"most":[108],"effective":[109],"strategies":[110],"under":[111],"extremely":[112],"low-resource":[113],"conditions.":[114],"Our":[115],"empirical":[116],"evaluations":[117],"provide":[118],"valuable":[119],"insights":[120],"for":[121],"future":[122],"federated":[126],"learning.":[127]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-14T00:00:00"}
