{"id":"https://openalex.org/W7130589273","doi":"https://doi.org/10.1109/fllm67465.2025.11391186","title":"Optimizing Federated Block-Term Tensor Regression: Strategy Comparisons and Applications","display_name":"Optimizing Federated Block-Term Tensor Regression: Strategy Comparisons and Applications","publication_year":2025,"publication_date":"2025-11-25","ids":{"openalex":"https://openalex.org/W7130589273","doi":"https://doi.org/10.1109/fllm67465.2025.11391186"},"language":null,"primary_location":{"id":"doi:10.1109/fllm67465.2025.11391186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fllm67465.2025.11391186","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 3rd International Conference on Foundation and Large Language Models (FLLM)","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/A5086373617","display_name":"Axel Faes","orcid":"https://orcid.org/0000-0002-1637-255X"},"institutions":[{"id":"https://openalex.org/I4210157022","display_name":"BioMed X Institute","ror":"https://ror.org/05drfac92","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210157022"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Axel Faes","raw_affiliation_strings":["DSI &#x0026; BIOMED UHasselt,Biomedical Data Sciences,Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DSI &#x0026; BIOMED UHasselt,Biomedical Data Sciences,Belgium","institution_ids":["https://openalex.org/I4210157022"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061462867","display_name":"Liesbet M. Peeters","orcid":"https://orcid.org/0000-0002-6066-3899"},"institutions":[{"id":"https://openalex.org/I4210157022","display_name":"BioMed X Institute","ror":"https://ror.org/05drfac92","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210157022"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Liesbet M. Peeters","raw_affiliation_strings":["DSI &#x0026; BIOMED UHasselt,Biomedical Data Sciences,Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DSI &#x0026; BIOMED UHasselt,Biomedical Data Sciences,Belgium","institution_ids":["https://openalex.org/I4210157022"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.81718782,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"327","last_page":"334"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.20749999582767487,"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":0.20749999582767487,"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/T11448","display_name":"Face recognition and analysis","score":0.06239999830722809,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.056299999356269836,"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":0.586899995803833},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5842999815940857},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5712000131607056},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.45509999990463257},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.3991999924182892},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.39579999446868896},{"id":"https://openalex.org/keywords/competition","display_name":"Competition (biology)","score":0.3880000114440918}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7617999911308289},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.586899995803833},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5842999815940857},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5712000131607056},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4912000000476837},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.45509999990463257},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44909998774528503},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40130001306533813},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.3991999924182892},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.39579999446868896},{"id":"https://openalex.org/C91306197","wikidata":"https://www.wikidata.org/wiki/Q45767","display_name":"Competition (biology)","level":2,"score":0.3880000114440918},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.38179999589920044},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.37059998512268066},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","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":0.2935999929904938},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fllm67465.2025.11391186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fllm67465.2025.11391186","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 3rd International Conference on Foundation and Large Language Models (FLLM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.4825271964073181,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1963826206","https://openalex.org/W1964486052","https://openalex.org/W1967696752","https://openalex.org/W2018282388","https://openalex.org/W2024165284","https://openalex.org/W2034778821","https://openalex.org/W2110503968","https://openalex.org/W2119412403","https://openalex.org/W2136002544","https://openalex.org/W2142501675","https://openalex.org/W2407714152","https://openalex.org/W2473418344","https://openalex.org/W2603380332","https://openalex.org/W2770037632","https://openalex.org/W2888508477","https://openalex.org/W2912213068","https://openalex.org/W2970606380","https://openalex.org/W2978677644","https://openalex.org/W3021654819","https://openalex.org/W3045674654","https://openalex.org/W3102434476","https://openalex.org/W3161343553","https://openalex.org/W4206320562","https://openalex.org/W4306247028","https://openalex.org/W4309364378","https://openalex.org/W4377982165","https://openalex.org/W4399531961"],"related_works":[],"abstract_inverted_index":{"Block-Term":[0,30],"Tensor":[1,31],"Regression":[2,32],"(BTTR)":[3],"is":[4],"a":[5,38,113],"multilin-ear":[6],"modeling":[7],"framework":[8],"suited":[9],"for":[10,116],"high-dimensional":[11],"biomedical":[12,121],"data,":[13],"but":[14],"its":[15],"centralized":[16],"implementation":[17],"conflicts":[18],"with":[19],"privacy":[20],"and":[21,59,83,109],"regulatory":[22],"constraints.":[23],"To":[24],"overcome":[25],"this,":[26],"we":[27],"propose":[28],"Federated":[29],"(FBTTR),":[33],"which":[34],"integrates":[35],"BTTR":[36],"into":[37],"federated":[39,46,107],"learning":[40,108],"setting.":[41],"We":[42],"systematically":[43],"evaluate":[44],"multiple":[45],"aggregation":[47],"strategies,":[48],"including":[49],"FedAvg,":[50],"FedYogi,":[51],"to":[52],"assess":[53],"their":[54],"effect":[55],"on":[56,62],"model":[57],"performance":[58],"stability.":[60],"Experiments":[61],"the":[63,70,80,101],"BCI":[64],"Competition":[65],"IV":[66],"dataset":[67],"demonstrate":[68],"that":[69],"choice":[71],"of":[72,103,119],"strategy":[73,104],"strongly":[74],"influences":[75],"predictive":[76],"accuracy:":[77],"FedAvg":[78],"yields":[79],"most":[81],"stable":[82],"accurate":[84],"results":[85],"across":[86],"subjects,":[87],"while":[88],"adaptive":[89],"methods":[90],"such":[91],"as":[92,112],"FedYogi":[93],"show":[94],"less":[95],"consistent":[96],"performance.":[97],"These":[98],"findings":[99],"highlight":[100],"importance":[102],"selection":[105],"in":[106],"establish":[110],"FBTTR":[111],"practical":[114],"approach":[115],"privacy-preserving":[117],"analysis":[118],"multi-institutional":[120],"data.":[122]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-20T00:00:00"}
