{"id":"https://openalex.org/W4401567311","doi":"https://doi.org/10.1109/tmm.2024.3443627","title":"Bayesian Uncertainty Calibration for Federated Time Series Analysis","display_name":"Bayesian Uncertainty Calibration for Federated Time Series Analysis","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4401567311","doi":"https://doi.org/10.1109/tmm.2024.3443627"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2024.3443627","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tmm.2024.3443627","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-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/A5061439975","display_name":"Chao Cai","orcid":"https://orcid.org/0009-0000-0574-0743"},"institutions":[{"id":"https://openalex.org/I59649739","display_name":"Jiangxi University of Finance and Economics","ror":"https://ror.org/03efmyj29","country_code":"CN","type":"education","lineage":["https://openalex.org/I59649739"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chao Cai","raw_affiliation_strings":["School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China"],"affiliations":[{"raw_affiliation_string":"School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China","institution_ids":["https://openalex.org/I59649739"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033446854","display_name":"Weide Liu","orcid":"https://orcid.org/0000-0002-9855-4479"},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weide Liu","raw_affiliation_strings":["Harvard Medical School, Harvard University, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Harvard Medical School, Harvard University, Cambridge, MA, USA","institution_ids":["https://openalex.org/I136199984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100662160","display_name":"Xue Xia","orcid":"https://orcid.org/0000-0002-2872-7151"},"institutions":[{"id":"https://openalex.org/I59649739","display_name":"Jiangxi University of Finance and Economics","ror":"https://ror.org/03efmyj29","country_code":"CN","type":"education","lineage":["https://openalex.org/I59649739"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xue Xia","raw_affiliation_strings":["School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China"],"affiliations":[{"raw_affiliation_string":"School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China","institution_ids":["https://openalex.org/I59649739"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080343454","display_name":"Zhenghua Chen","orcid":"https://orcid.org/0000-0002-1719-0328"},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Zhenghua Chen","raw_affiliation_strings":["Institute for Infocomm Research (I2R), Singapore"],"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research (I2R), Singapore","institution_ids":["https://openalex.org/I3005327000"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063013411","display_name":"Yuming Fang","orcid":"https://orcid.org/0000-0002-6946-3586"},"institutions":[{"id":"https://openalex.org/I59649739","display_name":"Jiangxi University of Finance and Economics","ror":"https://ror.org/03efmyj29","country_code":"CN","type":"education","lineage":["https://openalex.org/I59649739"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuming Fang","raw_affiliation_strings":["School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China"],"affiliations":[{"raw_affiliation_string":"School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China","institution_ids":["https://openalex.org/I59649739"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5061439975"],"corresponding_institution_ids":["https://openalex.org/I59649739"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14102907,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"26","issue":null,"first_page":"11151","last_page":"11163"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.987500011920929,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.987500011920929,"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"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9544000029563904,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9330000281333923,"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/computer-science","display_name":"Computer science","score":0.8105181455612183},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6619450449943542},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.6131200194358826},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5881046056747437},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5627106428146362},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5160434246063232},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34404733777046204},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33300673961639404},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2018519639968872},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0878860354423523}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8105181455612183},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6619450449943542},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.6131200194358826},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5881046056747437},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5627106428146362},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5160434246063232},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34404733777046204},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33300673961639404},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2018519639968872},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0878860354423523},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2024.3443627","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tmm.2024.3443627","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.550000011920929}],"awards":[{"id":"https://openalex.org/G1583650166","display_name":null,"funder_award_id":"62132006","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G206987818","display_name":null,"funder_award_id":"62441203","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3944859999","display_name":null,"funder_award_id":"62311530101","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W2053744708","https://openalex.org/W2104094955","https://openalex.org/W2120841219","https://openalex.org/W2131953535","https://openalex.org/W2187422466","https://openalex.org/W2333401465","https://openalex.org/W2530133016","https://openalex.org/W2535838896","https://openalex.org/W2793500099","https://openalex.org/W2801672217","https://openalex.org/W2887976372","https://openalex.org/W2908875359","https://openalex.org/W3006585575","https://openalex.org/W3034985049","https://openalex.org/W3093737097","https://openalex.org/W3096572172","https://openalex.org/W3112995907","https://openalex.org/W3115710758","https://openalex.org/W3126600466","https://openalex.org/W3162700561","https://openalex.org/W3199238138","https://openalex.org/W3208509800","https://openalex.org/W4213030023","https://openalex.org/W4226398914","https://openalex.org/W4281754984","https://openalex.org/W4285200947","https://openalex.org/W4285596634","https://openalex.org/W4289654476","https://openalex.org/W4312743418","https://openalex.org/W4312753596","https://openalex.org/W4312814827","https://openalex.org/W4312975111","https://openalex.org/W4313316160","https://openalex.org/W4323338370","https://openalex.org/W4327664521","https://openalex.org/W4384284064","https://openalex.org/W4385835750","https://openalex.org/W4386071546","https://openalex.org/W4386702786","https://openalex.org/W4387918039","https://openalex.org/W4387967999","https://openalex.org/W4388491946","https://openalex.org/W4389352474","https://openalex.org/W4390120136","https://openalex.org/W6605479355","https://openalex.org/W6728757088","https://openalex.org/W6759238902","https://openalex.org/W6763143685","https://openalex.org/W6764214684","https://openalex.org/W6796484261","https://openalex.org/W6797067896","https://openalex.org/W6803122252","https://openalex.org/W6803496579","https://openalex.org/W6804186094","https://openalex.org/W6860232588"],"related_works":["https://openalex.org/W1919101720","https://openalex.org/W4390822878","https://openalex.org/W96888382","https://openalex.org/W2041308758","https://openalex.org/W4386126592","https://openalex.org/W4392529072","https://openalex.org/W3171196943","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Deep":[0],"learning":[1,39,53,150,174,180],"models":[2],"for":[3,12,23],"time":[4,189],"series":[5,190],"analysis":[6],"often":[7,62],"require":[8],"large-scale":[9],"labeled":[10],"datasets":[11,17,57,208],"training.":[13],"However,":[14,55],"acquiring":[15],"such":[16],"is":[18],"cost-intensive":[19],"and":[20,30,66,116,146,192,206,212,217,221,235,244],"challenging,":[21],"particularly":[22],"individual":[24],"institutions.":[25],"To":[26,78,160],"overcome":[27],"this":[28,47,80],"challenge":[29],"concern":[31],"about":[32],"data":[33,76,91,130],"confidentiality":[34],"among":[35,132],"different":[36],"institutions,":[37],"federated":[38],"(FL)":[40],"servers":[41],"as":[42,103],"a":[43,51,84,181],"viable":[44],"solution":[45],"to":[46,70,74,87,100,184],"dilemma":[48],"by":[49,59,179],"offering":[50],"decentralized":[52],"framework.":[54],"the":[56,89,107,113,117,124,128,136,141,154,157,186,202,215,232,242],"collected":[58],"each":[60],"institution":[61],"suffer":[63],"from":[64],"imbalance":[65],"may":[67],"not":[68],"adhere":[69],"uniform":[71],"protocols,":[72],"leading":[73],"diverse":[75,129],"distributions.":[77],"address":[79,161],"problem,":[81],"we":[82,164],"design":[83],"global":[85,90],"model":[86],"approximate":[88,114],"distribution":[92,115,119],"of":[93,144,156,188],"all":[94],"participant":[95],"clients,":[96],"then":[97],"transfer":[98],"it":[99],"local":[101],"clients":[102,134],"an":[104,166],"induction":[105],"in":[106,121,123,148,214,240],"training":[108],"phase.":[109],"While":[110],"discrepancies":[111],"between":[112],"actual":[118],"result":[120],"uncertainty":[122,155,167,178,230],"predicted":[125],"results.":[126,159],"Moreover,":[127],"distributions":[131],"various":[133],"within":[135,231],"FL":[137,233],"framework,":[138],"combined":[139],"with":[140],"inherent":[142],"lack":[143],"reliability":[145],"interpretability":[147],"deep":[149,173],"models,":[151],"further":[152],"amplify":[153],"prediction":[158],"these":[162],"issues,":[163],"propose":[165],"calibration":[168],"method":[169],"based":[170],"on":[171,201],"Bayesian":[172],"techniques,":[175],"which":[176],"captures":[177],"fidelity":[182],"transformation":[183],"reconstruct":[185],"output":[187],"regression":[191,203,243],"classification":[193,207,245],"tasks,":[194,246],"utilizing":[195],"deterministic":[196],"pre-trained":[197],"models.":[198],"Extensive":[199],"experiments":[200],"dataset":[204],"(C-MAPSS)":[205],"(ESR,":[209],"Sleep-EDF,":[210],"HAR,":[211],"FD)":[213],"Independent":[216],"Identically":[218],"Distributed":[219],"(IID)":[220],"non-IID":[222],"settings":[223],"show":[224],"that":[225],"our":[226],"approach":[227],"effectively":[228],"calibrates":[229],"framework":[234],"facilitates":[236],"better":[237],"generalization":[238],"performance":[239],"both":[241],"achieving":[247],"state-of-the-art":[248],"performance.":[249]},"counts_by_year":[],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}
