{"id":"https://openalex.org/W4402351710","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651029","title":"FedTAIL: A Federated Learning Approach with Trans-Architecture Intermediate Links","display_name":"FedTAIL: A Federated Learning Approach with Trans-Architecture Intermediate Links","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402351710","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651029"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10651029","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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/A5107079509","display_name":"Dian Jiao","orcid":null},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dian Jiao","raw_affiliation_strings":["Harbin Institute of Technology,School of Computer Science and Technology,Harbin,China"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology,School of Computer Science and Technology,Harbin,China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100454217","display_name":"Jie Liu","orcid":"https://orcid.org/0000-0003-3974-2426"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Liu","raw_affiliation_strings":["Harbin Institute of Technology,School of Computer Science and Technology,Harbin,China"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology,School of Computer Science and Technology,Harbin,China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5107079509"],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12889506,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9983000159263611,"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/T11614","display_name":"Cloud Data Security Solutions","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.7744296789169312},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.6799623966217041},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.4027244746685028},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.3602953851222992}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7744296789169312},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.6799623966217041},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.4027244746685028},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3602953851222992},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10651029","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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":59,"referenced_works":["https://openalex.org/W1536719639","https://openalex.org/W1821462560","https://openalex.org/W2194775991","https://openalex.org/W2549139847","https://openalex.org/W2558580397","https://openalex.org/W2793241364","https://openalex.org/W2903471046","https://openalex.org/W2912893715","https://openalex.org/W2936864631","https://openalex.org/W2964051877","https://openalex.org/W2982157312","https://openalex.org/W2986015886","https://openalex.org/W2990789643","https://openalex.org/W2995022099","https://openalex.org/W2997006708","https://openalex.org/W3004127093","https://openalex.org/W3034368386","https://openalex.org/W3035453001","https://openalex.org/W3080934299","https://openalex.org/W3086152015","https://openalex.org/W3091635927","https://openalex.org/W3094163844","https://openalex.org/W3094502228","https://openalex.org/W3099225546","https://openalex.org/W3134307371","https://openalex.org/W3138154797","https://openalex.org/W3138516171","https://openalex.org/W3143320354","https://openalex.org/W3161357657","https://openalex.org/W3196371845","https://openalex.org/W3203532272","https://openalex.org/W4213019189","https://openalex.org/W4224227775","https://openalex.org/W4283796083","https://openalex.org/W4285876308","https://openalex.org/W4312379976","https://openalex.org/W4312560592","https://openalex.org/W4312797616","https://openalex.org/W4318619660","https://openalex.org/W4385245566","https://openalex.org/W4387105517","https://openalex.org/W4387353472","https://openalex.org/W4390189778","https://openalex.org/W6638523607","https://openalex.org/W6728757088","https://openalex.org/W6757139170","https://openalex.org/W6759238902","https://openalex.org/W6762718338","https://openalex.org/W6768537144","https://openalex.org/W6769906912","https://openalex.org/W6770590064","https://openalex.org/W6776213126","https://openalex.org/W6779269186","https://openalex.org/W6780224944","https://openalex.org/W6782937392","https://openalex.org/W6784239669","https://openalex.org/W6784333009","https://openalex.org/W6784838754","https://openalex.org/W6791141078"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W2038503502"],"abstract_inverted_index":{"Federated":[0],"Learning":[1],"(FL)":[2],"allows":[3],"multiple":[4],"clients":[5,50,107,133],"to":[6,56,81,100,134,155],"collaboratively":[7],"train":[8],"deep":[9],"learning":[10,123],"models":[11,29,88],"while":[12],"preserving":[13],"the":[14,24,42,72,147,167,178],"privacy":[15],"of":[16,31,37,74,140],"local":[17,142,157],"data,":[18],"which":[19],"is":[20,79,96],"achieved":[21],"by":[22],"aggregating":[23],"gradient":[25],"information":[26],"from":[27,137],"clients'":[28],"instead":[30],"using":[32,108],"raw":[33],"data.":[34],"The":[35],"success":[36],"FL":[38,52,95],"depends":[39],"significantly":[40],"on":[41,160],"coordinated":[43],"scheduling":[44],"among":[45,49],"clients.":[46,170],"However,":[47],"heterogeneity":[48],"in":[51],"typically":[53],"poses":[54],"challenges":[55],"generalization":[57],"performance,":[58],"such":[59],"as":[60,69,149],"unbalanced":[61],"data":[62],"distribution":[63],"and":[64,89,152,169],"different":[65],"model":[66,124,131],"architectures.":[67,183],"Particularly,":[68],"we":[70,118],"enter":[71],"era":[73],"giant":[75],"AI":[76],"models,":[77,143],"it":[78],"vital":[80],"assess":[82],"FL\u2019s":[83],"compatibility":[84],"with":[85,125],"both":[86],"transformer":[87],"traditional":[90],"convolutional":[91],"networks.":[92],"Knowledge":[93],"Distillation-based":[94],"considered":[97],"a":[98,120],"solution":[99],"address":[101],"heterogeneity,":[102],"but":[103],"transferring":[104],"knowledge":[105],"within":[106],"only":[109],"final":[110],"predictions":[111],"may":[112],"impact":[113],"efficiency.":[114],"To":[115],"enhance":[116],"generalization,":[117],"propose":[119],"representation-based":[121],"federated":[122],"Trans-Architecture":[126],"Intermediate":[127],"Linking":[128],"(FedTAIL).":[129],"Our":[130],"enables":[132],"share":[135],"representations":[136],"intermediate":[138],"layers":[139],"their":[141],"aggregates":[144],"them":[145,154],"at":[146],"server":[148,168],"global":[150],"representations,":[151],"uses":[153],"regularize":[156],"training":[158],"based":[159],"similarities":[161],"for":[162],"all":[163],"potential":[164],"links":[165],"between":[166],"Empirical":[171],"results":[172],"verify":[173],"that":[174],"our":[175],"framework":[176],"achieves":[177],"best":[179],"performance":[180],"across":[181],"various":[182]},"counts_by_year":[],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}
