{"id":"https://openalex.org/W7129058380","doi":"https://doi.org/10.48550/arxiv.2602.13004","title":"Towards Uncertainty-Aware Federated Granger Causal Learning","display_name":"Towards Uncertainty-Aware Federated Granger Causal Learning","publication_year":2026,"publication_date":"2026-02-13","ids":{"openalex":"https://openalex.org/W7129058380","doi":"https://doi.org/10.48550/arxiv.2602.13004"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.13004","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.13004","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2602.13004","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136800441","display_name":"Ayush Mohanty","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohanty, Ayush","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065890596","display_name":"Nazal Mohamed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohamed, Nazal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5054372641","display_name":"Nagi Gebraeel","orcid":"https://orcid.org/0000-0001-7337-2401"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gebraeel, Nagi","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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.3068000078201294,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.3068000078201294,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.1647000014781952,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.08720000088214874,"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/interpretability","display_name":"Interpretability","score":0.7942000031471252},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5343000292778015},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.5181999802589417},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.43299999833106995},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.42179998755455017},{"id":"https://openalex.org/keywords/interdependence","display_name":"Interdependence","score":0.41839998960494995},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.3993000090122223},{"id":"https://openalex.org/keywords/granger-causality","display_name":"Granger causality","score":0.351500004529953},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.3481000065803528}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7942000031471252},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6431999802589417},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5343000292778015},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.5181999802589417},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.43299999833106995},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.42179998755455017},{"id":"https://openalex.org/C185874996","wikidata":"https://www.wikidata.org/wiki/Q269699","display_name":"Interdependence","level":2,"score":0.41839998960494995},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.40209999680519104},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4004000127315521},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.3993000090122223},{"id":"https://openalex.org/C129824826","wikidata":"https://www.wikidata.org/wiki/Q2630107","display_name":"Granger causality","level":2,"score":0.351500004529953},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3481000065803528},{"id":"https://openalex.org/C177803969","wikidata":"https://www.wikidata.org/wiki/Q29205","display_name":"Uncertainty analysis","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.32019999623298645},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.27619999647140503},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27459999918937683},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C176147448","wikidata":"https://www.wikidata.org/wiki/Q1889114","display_name":"Sensitivity analysis","level":3,"score":0.27230000495910645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27079999446868896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25850000977516174},{"id":"https://openalex.org/C2778865114","wikidata":"https://www.wikidata.org/wiki/Q7882489","display_name":"Uncertain data","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C123614077","wikidata":"https://www.wikidata.org/wiki/Q1364905","display_name":"Propagation of uncertainty","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2526000142097473}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.13004","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.13004","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.13004","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.13004","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6573256850242615}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Granger":[0,32],"causality":[1,33],"recovers":[2,35],"directed":[3],"interactions":[4,37,158],"from":[5,159],"time-series":[6],"data,":[7],"but":[8],"in":[9],"many":[10],"distributed":[11],"systems,":[12],"the":[13,25,77,86,90,104,109,120,134,138,170,175,184],"data":[14,127],"are":[15],"vertically":[16],"partitioned":[17],"across":[18,177],"clients,":[19],"with":[20,50],"each":[21],"client":[22,110,126],"observing":[23],"only":[24,124],"variables":[26],"of":[27,54,133],"its":[28],"own":[29],"subsystem.":[30],"Federated":[31],"(FedGC)":[34],"cross-client":[36,65,157],"without":[38,58],"sharing":[39],"raw":[40],"data.":[41],"Existing":[42],"FedGC":[43,78],"methods,":[44],"however,":[45],"return":[46],"deterministic":[47],"point":[48],"estimates":[49],"no":[51],"calibrated":[52],"measure":[53],"uncertainty,":[55],"leaving":[56],"operators":[57],"a":[59,149],"principled":[60],"basis":[61],"for":[62,85,103],"identifying":[63],"reliable":[64],"interactions.":[66],"We":[67,80],"address":[68],"this":[69,144],"limitation":[70],"by":[71,89],"characterizing":[72],"how":[73],"uncertainty":[74,122,172],"propagates":[75],"through":[76],"framework.":[79],"derive":[81],"closed-form":[82,101],"covariance":[83],"recursions":[84],"cross-covariances":[87],"induced":[88],"coupled":[91],"client-server":[92],"feedback":[93],"loop,":[94],"and":[95,111,130,165],"establish":[96],"spectral-radius-based":[97],"convergence":[98],"conditions":[99],"yielding":[100],"expressions":[102],"steady-state":[105,121],"variances":[106],"at":[107],"both":[108],"server.":[112],"Under":[113],"mild":[114],"stability":[115],"conditions,":[116],"we":[117,147],"prove":[118],"that":[119,154,169],"depends":[123],"on":[125,137,143,163],"statistics":[128],"(aleatoric)":[129],"is":[131],"independent":[132],"priors":[135],"placed":[136],"model":[139],"parameters":[140],"(epistemic).":[141],"Building":[142],"asymptotic":[145],"characterization,":[146],"construct":[148],"post-training":[150],"hypothesis":[151],"testing":[152],"procedure":[153],"separates":[155],"genuine":[156],"spurious":[160],"edges.":[161],"Experiments":[162],"synthetic":[164],"real-world":[166],"datasets":[167],"show":[168],"predicted":[171],"propagation":[173],"matches":[174],"theory":[176],"multiple":[178],"operating":[179],"regimes,":[180],"while":[181],"consistently":[182],"outperforming":[183],"state-of-the-art":[185],"federated":[186],"causal":[187],"structure":[188],"learning":[189],"baselines.":[190]},"counts_by_year":[],"updated_date":"2026-08-16T07:02:28.622633","created_date":"2026-02-17T00:00:00"}