{"id":"https://openalex.org/W4416251553","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228905","title":"Federated Low-Rank Tensor Estimation for Multimodal Image Reconstruction","display_name":"Federated Low-Rank Tensor Estimation for Multimodal Image Reconstruction","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251553","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228905"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228905","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228905","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 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/A5064307110","display_name":"Anh Van Nguyen","orcid":"https://orcid.org/0000-0003-0730-6654"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Anh Van Nguyen","raw_affiliation_strings":["Northwestern University,Evanston,USA"],"affiliations":[{"raw_affiliation_string":"Northwestern University,Evanston,USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013049879","display_name":"Diego Klabjan","orcid":"https://orcid.org/0000-0003-4213-9281"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Diego Klabjan","raw_affiliation_strings":["Northwestern University,Evanston,USA"],"affiliations":[{"raw_affiliation_string":"Northwestern University,Evanston,USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084098918","display_name":"Minseok Ryu","orcid":"https://orcid.org/0000-0002-1954-7722"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Minseok Ryu","raw_affiliation_strings":["Arizona State University,Tempe,USA"],"affiliations":[{"raw_affiliation_string":"Arizona State University,Tempe,USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101620451","display_name":"Kibaek Kim","orcid":"https://orcid.org/0000-0001-6239-172X"},"institutions":[{"id":"https://openalex.org/I1282105669","display_name":"Argonne National Laboratory","ror":"https://ror.org/05gvnxz63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282105669","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kibaek Kim","raw_affiliation_strings":["Argonne National Laboratory,Lemont,USA"],"affiliations":[{"raw_affiliation_string":"Argonne National Laboratory,Lemont,USA","institution_ids":["https://openalex.org/I1282105669"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021352320","display_name":"Zichao Wendy Di","orcid":"https://orcid.org/0000-0002-4131-9363"},"institutions":[{"id":"https://openalex.org/I1282105669","display_name":"Argonne National Laboratory","ror":"https://ror.org/05gvnxz63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282105669","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zichao Di","raw_affiliation_strings":["Argonne National Laboratory,Lemont,USA"],"affiliations":[{"raw_affiliation_string":"Argonne National Laboratory,Lemont,USA","institution_ids":["https://openalex.org/I1282105669"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5064307110"],"corresponding_institution_ids":["https://openalex.org/I111979921"],"apc_list":null,"apc_paid":null,"fwci":1.4815,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.8605852,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.4020000100135803,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.4020000100135803,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.3993000090122223,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.07180000096559525,"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/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.5565000176429749},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.5457000136375427},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5070000290870667},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.4885999858379364},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4302000105381012},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.41339999437332153},{"id":"https://openalex.org/keywords/tensor-decomposition","display_name":"Tensor decomposition","score":0.396699994802475},{"id":"https://openalex.org/keywords/tucker-decomposition","display_name":"Tucker decomposition","score":0.38420000672340393},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.3441999852657318}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6687999963760376},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5629000067710876},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.5565000176429749},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.5457000136375427},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5070000290870667},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.4885999858379364},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4302000105381012},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4153999984264374},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.41339999437332153},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.396699994802475},{"id":"https://openalex.org/C42704193","wikidata":"https://www.wikidata.org/wiki/Q7851097","display_name":"Tucker decomposition","level":4,"score":0.38420000672340393},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.3441999852657318},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.33809998631477356},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33329999446868896},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.3296000063419342},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.32589998841285706},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3156000077724457},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3018999993801117},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29019999504089355},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.28299999237060547},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.2635999917984009},{"id":"https://openalex.org/C2778258933","wikidata":"https://www.wikidata.org/wiki/Q16918986","display_name":"Decomposition method (queueing theory)","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25600001215934753},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2538999915122986},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228905","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228905","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320332359","display_name":"Office of Science","ror":"https://ror.org/00mmn6b08"},{"id":"https://openalex.org/F4320337506","display_name":"Advanced Scientific Computing Research","ror":"https://ror.org/0012c7r22"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1580176742","https://openalex.org/W2024165284","https://openalex.org/W2055913665","https://openalex.org/W2080843093","https://openalex.org/W2086261631","https://openalex.org/W2117756735","https://openalex.org/W2136002544","https://openalex.org/W2164566335","https://openalex.org/W2227107777","https://openalex.org/W2738069262","https://openalex.org/W2989289980","https://openalex.org/W2989402453","https://openalex.org/W3007235251","https://openalex.org/W3127698310","https://openalex.org/W3134690344","https://openalex.org/W3139119893","https://openalex.org/W3155581945","https://openalex.org/W4318822975","https://openalex.org/W4327650177","https://openalex.org/W4362694216","https://openalex.org/W4385488764","https://openalex.org/W4386263535","https://openalex.org/W4387211931","https://openalex.org/W4388756711","https://openalex.org/W4389776620","https://openalex.org/W4392909918","https://openalex.org/W4393219236","https://openalex.org/W4405754229","https://openalex.org/W4410582635"],"related_works":[],"abstract_inverted_index":{"Low-rank":[0],"tensor":[1,31],"estimation":[2],"offers":[3],"a":[4,61],"powerful":[5],"approach":[6,82],"to":[7,17,41,52,76,93,119],"addressing":[8],"high-dimensional":[9],"data":[10],"challenges":[11],"and":[12,49,73,87,115],"can":[13],"substantially":[14],"improve":[15],"solutions":[16],"ill-posed":[18],"inverse":[19,128],"problems,":[20],"such":[21],"as":[22],"image":[23,63],"reconstruction":[24,64,113],"under":[25],"noisy":[26],"or":[27,102],"undersampled":[28],"conditions.":[29],"Meanwhile,":[30],"decomposition":[32,96],"has":[33],"gained":[34],"prominence":[35],"in":[36,44,130],"federated":[37,62],"learning":[38],"(FL)":[39],"due":[40],"its":[42,50,124],"effectiveness":[43],"exploiting":[45],"latent":[46],"space":[47],"structure":[48],"capacity":[51],"enhance":[53],"communication":[54,103,116],"efficiency.":[55],"In":[56],"this":[57],"paper,":[58],"we":[59],"present":[60],"method":[65,110],"that":[66,108],"applies":[67],"Tucker":[68],"decomposition,":[69],"incorporating":[70],"joint":[71],"factorization":[72],"randomized":[74],"sketching":[75],"manage":[77],"large-scale,":[78],"multimodal":[79,127],"data.":[80],"Our":[81],"avoids":[83],"reconstructing":[84],"full-size":[85],"tensors":[86],"supports":[88],"heterogeneous":[89],"ranks,":[90],"allowing":[91],"clients":[92],"select":[94],"personalized":[95],"ranks":[97],"based":[98],"on":[99],"prior":[100],"knowledge":[101],"capacity.":[104],"Numerical":[105],"results":[106],"demonstrate":[107],"our":[109],"achieves":[111],"superior":[112],"quality":[114],"compression":[117],"compared":[118],"existing":[120],"approaches,":[121],"thereby":[122],"highlighting":[123],"potential":[125],"for":[126],"problems":[129],"the":[131],"FL":[132],"setting.":[133]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-14T00:00:00"}
