{"id":"https://openalex.org/W3084967198","doi":"https://doi.org/10.1109/tsp.2022.3168490","title":"Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent","display_name":"Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W3084967198","doi":"https://doi.org/10.1109/tsp.2022.3168490","mag":"3084967198"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2022.3168490","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2022.3168490","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://kclpure.kcl.ac.uk/portal/en/publications/4753f103-9992-4b4e-a9d6-fc91fcf270ef","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060023455","display_name":"Rahif Kassab","orcid":"https://orcid.org/0000-0002-0637-5060"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Rahif Kassab","raw_affiliation_strings":["King’s Communications, Learning and Information Processing (KCLIP) Lab King’s College London, London, U.K"],"affiliations":[{"raw_affiliation_string":"King’s Communications, Learning and Information Processing (KCLIP) Lab King’s College London, London, U.K","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017736224","display_name":"Osvaldo Simeone","orcid":"https://orcid.org/0000-0001-9898-3209"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Osvaldo Simeone","raw_affiliation_strings":["King’s Communications, Learning and Information Processing (KCLIP) Lab King’s College London, London, U.K"],"affiliations":[{"raw_affiliation_string":"King’s Communications, Learning and Information Processing (KCLIP) Lab King’s College London, London, U.K","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5060023455"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.784,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.91413132,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"70","issue":null,"first_page":"2180","last_page":"2192"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9994999766349792,"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":0.9994999766349792,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9980999827384949,"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":0.9961000084877014,"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/frequentist-inference","display_name":"Frequentist inference","score":0.6690293550491333},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6512454748153687},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6492086052894592},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.6170631647109985},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5717901587486267},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5496880412101746},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5022997856140137},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4910016357898712},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.47447946667671204},{"id":"https://openalex.org/keywords/free-energy-principle","display_name":"Free energy principle","score":0.472003698348999},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.4513128101825714},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"Distributed learning","score":0.43373268842697144},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3846525251865387},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.27175986766815186},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2565068304538727},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.11845281720161438}],"concepts":[{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.6690293550491333},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6512454748153687},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6492086052894592},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.6170631647109985},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5717901587486267},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5496880412101746},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5022997856140137},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4910016357898712},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.47447946667671204},{"id":"https://openalex.org/C33553690","wikidata":"https://www.wikidata.org/wiki/Q17014702","display_name":"Free energy principle","level":2,"score":0.472003698348999},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.4513128101825714},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.43373268842697144},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3846525251865387},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27175986766815186},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2565068304538727},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.11845281720161438},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tsp.2022.3168490","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2022.3168490","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:kclpure.kcl.ac.uk:openaire/4753f103-9992-4b4e-a9d6-fc91fcf270ef","is_oa":true,"landing_page_url":"https://kclpure.kcl.ac.uk/portal/en/publications/4753f103-9992-4b4e-a9d6-fc91fcf270ef","pdf_url":null,"source":{"id":"https://openalex.org/S4306400216","display_name":"Research Portal (King's College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I183935753","host_organization_name":"King's College London","host_organization_lineage":["https://openalex.org/I183935753"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Kassab, R & Simeone, O 2022, 'Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent', IEEE Transactions on Signal Processing, vol. 70, pp. 2180-2192. https://doi.org/10.1109/TSP.2022.3168490","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:kclpure.kcl.ac.uk:publications/4753f103-9992-4b4e-a9d6-fc91fcf270ef","is_oa":true,"landing_page_url":"http://www.scopus.com/inward/record.url?scp=85129369928&partnerID=8YFLogxK","pdf_url":null,"source":{"id":"https://openalex.org/S4306400216","display_name":"Research Portal (King's College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I183935753","host_organization_name":"King's College London","host_organization_lineage":["https://openalex.org/I183935753"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Kassab, R & Simeone, O 2022, 'Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent', IEEE Transactions on Signal Processing, vol. 70, pp. 2180-2192. https://doi.org/10.1109/TSP.2022.3168490","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"pmh:oai:kclpure.kcl.ac.uk:openaire/4753f103-9992-4b4e-a9d6-fc91fcf270ef","is_oa":true,"landing_page_url":"https://kclpure.kcl.ac.uk/portal/en/publications/4753f103-9992-4b4e-a9d6-fc91fcf270ef","pdf_url":null,"source":{"id":"https://openalex.org/S4306400216","display_name":"Research Portal (King's College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I183935753","host_organization_name":"King's College London","host_organization_lineage":["https://openalex.org/I183935753"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Kassab, R & Simeone, O 2022, 'Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent', IEEE Transactions on Signal Processing, vol. 70, pp. 2180-2192. https://doi.org/10.1109/TSP.2022.3168490","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.6399999856948853}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334678","display_name":"European Research Council","ror":"https://ror.org/0472cxd90"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":118,"referenced_works":["https://openalex.org/W3401598","https://openalex.org/W1506806321","https://openalex.org/W1567512734","https://openalex.org/W1595104989","https://openalex.org/W1599263113","https://openalex.org/W1607198972","https://openalex.org/W1719489212","https://openalex.org/W1821462560","https://openalex.org/W1866929664","https://openalex.org/W1970789124","https://openalex.org/W1994655188","https://openalex.org/W2091891040","https://openalex.org/W2098824882","https://openalex.org/W2104347495","https://openalex.org/W2130797782","https://openalex.org/W2150796457","https://openalex.org/W2164613257","https://openalex.org/W2165363188","https://openalex.org/W2165478976","https://openalex.org/W2167433878","https://openalex.org/W2168947765","https://openalex.org/W2171911691","https://openalex.org/W2259016306","https://openalex.org/W2305001871","https://openalex.org/W2441221256","https://openalex.org/W2541884796","https://openalex.org/W2599969863","https://openalex.org/W2797937785","https://openalex.org/W2798720628","https://openalex.org/W2807006176","https://openalex.org/W2883808747","https://openalex.org/W2903180926","https://openalex.org/W2914328083","https://openalex.org/W2921789809","https://openalex.org/W2935209189","https://openalex.org/W2946573451","https://openalex.org/W2949377959","https://openalex.org/W2962752165","https://openalex.org/W2962817476","https://openalex.org/W2963161304","https://openalex.org/W2963932270","https://openalex.org/W2963956018","https://openalex.org/W2963984540","https://openalex.org/W2964121937","https://openalex.org/W2964212410","https://openalex.org/W2964231067","https://openalex.org/W2971130081","https://openalex.org/W2971330699","https://openalex.org/W2981679558","https://openalex.org/W2992140536","https://openalex.org/W2995022099","https://openalex.org/W3004158152","https://openalex.org/W3014596384","https://openalex.org/W3015901293","https://openalex.org/W3021435406","https://openalex.org/W3021654819","https://openalex.org/W3021861822","https://openalex.org/W3028382483","https://openalex.org/W3036693463","https://openalex.org/W3038426846","https://openalex.org/W3041243646","https://openalex.org/W3043426275","https://openalex.org/W3043723611","https://openalex.org/W3044765474","https://openalex.org/W3047304572","https://openalex.org/W3080192287","https://openalex.org/W3083250313","https://openalex.org/W3084298117","https://openalex.org/W3084967198","https://openalex.org/W3089082234","https://openalex.org/W3099663651","https://openalex.org/W3102449016","https://openalex.org/W3105122387","https://openalex.org/W3113075536","https://openalex.org/W3123743074","https://openalex.org/W3134774296","https://openalex.org/W3140968660","https://openalex.org/W3153501397","https://openalex.org/W3194214002","https://openalex.org/W3204468048","https://openalex.org/W4205433696","https://openalex.org/W4287661303","https://openalex.org/W4289242671","https://openalex.org/W4294106961","https://openalex.org/W6600144256","https://openalex.org/W6638523607","https://openalex.org/W6639186783","https://openalex.org/W6683926941","https://openalex.org/W6684432121","https://openalex.org/W6684809622","https://openalex.org/W6684877843","https://openalex.org/W6692179066","https://openalex.org/W6718763946","https://openalex.org/W6725708968","https://openalex.org/W6728757088","https://openalex.org/W6736966227","https://openalex.org/W6739651123","https://openalex.org/W6740094179","https://openalex.org/W6750300694","https://openalex.org/W6752029299","https://openalex.org/W6754509688","https://openalex.org/W6756414605","https://openalex.org/W6759238902","https://openalex.org/W6760416189","https://openalex.org/W6760907187","https://openalex.org/W6763048141","https://openalex.org/W6763497089","https://openalex.org/W6766092863","https://openalex.org/W6766477559","https://openalex.org/W6768106036","https://openalex.org/W6770580377","https://openalex.org/W6773712235","https://openalex.org/W6776730200","https://openalex.org/W6779658066","https://openalex.org/W6779815396","https://openalex.org/W6783948749","https://openalex.org/W6784336702","https://openalex.org/W6802235389"],"related_works":["https://openalex.org/W4206903459","https://openalex.org/W2754816816","https://openalex.org/W4366280654","https://openalex.org/W3160167280","https://openalex.org/W4231621013","https://openalex.org/W4362706668","https://openalex.org/W3008318776","https://openalex.org/W1977633006","https://openalex.org/W1971945429","https://openalex.org/W2041416246"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"Distributed":[3],"Stein":[4],"Variational":[5],"Gradient":[6],"Descent":[7],"(DSVGD),":[8],"a":[9,20,28,49,71],"non-parametric":[10],"generalized":[11],"Bayesian":[12,93],"inference":[13],"framework":[14],"for":[15],"federated":[16,94],"learning.":[17],"DSVGD":[18,69,83],"maintains":[19],"number":[21,66,79,107],"of":[22,36,51,57,67,80,99,108],"non-random":[23],"and":[24,46,78,92,101,114],"interacting":[25],"particles":[26,42],"at":[27],"central":[29],"server":[30],"to":[31,86,89,105],"represent":[32],"the":[33,37,54,59,65,106],"current":[34],"iterate":[35],"model":[38],"global":[39,60],"posterior.":[40],"The":[41],"are":[43],"iteratively":[44],"downloaded":[45],"updated":[47],"by":[48],"subset":[50],"agents":[52],"with":[53,103],"end":[55],"goal":[56],"minimizing":[58],"free":[61],"energy.":[62],"By":[63],"varying":[64],"particles,":[68],"enables":[70],"flexible":[72],"trade-off":[73],"between":[74],"per-iteration":[75],"communication":[76,81],"load":[77],"rounds.":[82],"is":[84],"shown":[85],"compare":[87],"favorably":[88],"benchmark":[90],"frequentist":[91],"learning":[95],"strategies":[96],"in":[97],"terms":[98],"accuracy":[100],"scalability":[102],"respect":[104],"agents,":[109],"while":[110],"also":[111],"providing":[112],"well-calibrated,":[113],"hence":[115],"trustworthy,":[116],"predictions.":[117]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}