{"id":"https://openalex.org/W7160181011","doi":"https://doi.org/10.48550/arxiv.2605.00500","title":"Scaling Federated Linear Contextual Bandits via Sketching","display_name":"Scaling Federated Linear Contextual Bandits via Sketching","publication_year":2026,"publication_date":"2026-05-01","ids":{"openalex":"https://openalex.org/W7160181011","doi":"https://doi.org/10.48550/arxiv.2605.00500"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.00500","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00500","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.00500","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111136884","display_name":"Hantao Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Hantao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135282582","display_name":"Hong Xie","orcid":"https://orcid.org/0000-0001-7935-7210"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Hong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135161079","display_name":"Xutong Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xutong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135198109","display_name":"Defu Lian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lian, Defu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.8934999704360962,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.8934999704360962,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.02319999970495701,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.019600000232458115,"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/regret","display_name":"Regret","score":0.7128999829292297},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6205000281333923},{"id":"https://openalex.org/keywords/upload","display_name":"Upload","score":0.6068000197410583},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5092999935150146},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.46889999508857727},{"id":"https://openalex.org/keywords/communication-complexity","display_name":"Communication complexity","score":0.44279998540878296},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.43849998712539673},{"id":"https://openalex.org/keywords/sketch","display_name":"Sketch","score":0.43720000982284546},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.4325000047683716},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.42080000042915344}],"concepts":[{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.7128999829292297},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7084000110626221},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6205000281333923},{"id":"https://openalex.org/C71901391","wikidata":"https://www.wikidata.org/wiki/Q7126699","display_name":"Upload","level":2,"score":0.6068000197410583},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5092999935150146},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.46889999508857727},{"id":"https://openalex.org/C179145077","wikidata":"https://www.wikidata.org/wiki/Q5154130","display_name":"Communication complexity","level":2,"score":0.44279998540878296},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.43849998712539673},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.43720000982284546},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.43619999289512634},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.4325000047683716},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.42080000042915344},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.4163999855518341},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4056999981403351},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.4027000069618225},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.39800000190734863},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.39469999074935913},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.3596999943256378},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.34940001368522644},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.34369999170303345},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32899999618530273},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.2851000130176544},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C17349429","wikidata":"https://www.wikidata.org/wiki/Q1049914","display_name":"Matrix multiplication","level":3,"score":0.27799999713897705},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C176181172","wikidata":"https://www.wikidata.org/wiki/Q3490301","display_name":"Nondeterministic algorithm","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C117160843","wikidata":"https://www.wikidata.org/wiki/Q338652","display_name":"Sublinear function","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.25540000200271606},{"id":"https://openalex.org/C42747912","wikidata":"https://www.wikidata.org/wiki/Q1048447","display_name":"Multiplicative function","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.25040000677108765},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.00500","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00500","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.00500","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00500","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"federated":[1,113],"contextual":[2,114],"linear":[3,115],"bandits,":[4],"high":[5],"data":[6],"dimensionality":[7],"incurs":[8],"prohibitive":[9,66],"computation":[10,19,51],"and":[11,20,72,101,122,191,200],"communication":[12,90,126,201],"costs:":[13],"local":[14,120],"agents":[15],"perform":[16],"$O(d^3)$-time":[17],"determinant":[18,60,70],"upload":[21,100],"$O(d^2)$":[22,105],"parameters,":[23],"making":[24],"existing":[25],"algorithms":[26],"unscalable,":[27],"where":[28,81,154],"$d$":[29],"is":[30,84,156],"the":[31,50,59,65,85,89,119,124,133,137,157,161,165,171,174,177,183],"dimension":[32],"of":[33,68,151,164,173,213],"data.":[34],"To":[35],"relieve":[36],"these":[37],"scaling":[38],"bottlenecks,":[39],"this":[40,130],"paper":[41],"proposes":[42],"Federated":[43],"Sketch":[44],"Contextual":[45],"Linear":[46],"Bandits":[47],"(FSCLB).":[48],"On":[49,88],"side,":[52,91],"FSCLB":[53,92,128,146,196],"uses":[54],"SVD":[55],"to":[56,77,106,143,180],"indirectly":[57],"obtain":[58],"required":[61],"for":[62],"communication,":[63],"eliminating":[64],"cost":[67],"direct":[69],"calculation":[71],"cutting":[73],"complexity":[74],"from":[75,104],"$O(d^3)$":[76],"$O(l^2d)$":[78],"per":[79],"round,":[80],"$l<":[82],"d$":[83],"sketch":[86,110,138],"size.":[87],"introduces":[93],"a":[94,148,210],"double-sketch":[95],"strategy":[96],"that":[97,195],"reduces":[98,198],"both":[99,189],"download":[102],"costs":[103,202],"$O(ld)$.":[107],"Naively":[108],"involving":[109],"update":[111],"into":[112],"bandits":[116],"can":[117],"destroy":[118],"increment":[121],"invalidate":[123],"asynchronous":[125],"condition;":[127],"solves":[129],"by":[131,160,203],"replacing":[132],"covariance":[134,166,175],"matrix":[135,139],"with":[136],"when":[140,168],"deciding":[141],"whether":[142],"communicate.":[144],"Theoretically,":[145],"achieves":[147],"regret":[149],"bound":[150,178],"$\\widetilde{O}":[152],"((\\sqrt{d}+\\sqrt{M\\varepsilon_l})\\sqrt{lT})$,":[153],"$\\varepsilon_l$":[155],"upper":[158],"bounded":[159],"spectral":[162],"tail":[163],"matrix;":[167],"$l$":[169],"exceeds":[170],"rank":[172],"matrix,":[176],"simplifies":[179],"$\\widetilde{O}(\\sqrt{ldT})$,":[181],"matching":[182],"optimal":[184],"no-sketch":[185],"regret.":[186],"Experiments":[187],"on":[188],"synthetic":[190],"real-world":[192],"datasets":[193],"show":[194],"significantly":[197],"computational":[199],"over":[204],"90":[205],"\\%":[206],"while":[207],"sacrificing":[208],"only":[209],"negligible":[211],"amount":[212],"cumulative":[214],"reward.":[215]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-05T00:00:00"}