{"id":"https://openalex.org/W7161034781","doi":"https://doi.org/10.48550/arxiv.2605.11684","title":"Partial Model Sharing Improves Byzantine Resilience in Federated Conformal Prediction","display_name":"Partial Model Sharing Improves Byzantine Resilience in Federated Conformal Prediction","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7161034781","doi":"https://doi.org/10.48550/arxiv.2605.11684"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.11684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11684","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.11684","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092794903","display_name":"Ehsan Lari","orcid":"https://orcid.org/0000-0003-2635-0028"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lari, Ehsan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136037309","display_name":"Reza Arablouei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arablouei, Reza","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136036575","display_name":"Stefan Werner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Werner, Stefan","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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.8501999974250793,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.8501999974250793,"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":0.02800000086426735,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.026399999856948853,"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/resilience","display_name":"Resilience (materials science)","score":0.677299976348877},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.5302000045776367},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5131999850273132},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5105999708175659},{"id":"https://openalex.org/keywords/conformal-map","display_name":"Conformal map","score":0.5011000037193298},{"id":"https://openalex.org/keywords/byzantine-architecture","display_name":"Byzantine architecture","score":0.4129999876022339},{"id":"https://openalex.org/keywords/byzantine-fault-tolerance","display_name":"Byzantine fault tolerance","score":0.36390000581741333}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7182999849319458},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.677299976348877},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.5302000045776367},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5131999850273132},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5105999708175659},{"id":"https://openalex.org/C98214594","wikidata":"https://www.wikidata.org/wiki/Q850275","display_name":"Conformal map","level":2,"score":0.5011000037193298},{"id":"https://openalex.org/C104562893","wikidata":"https://www.wikidata.org/wiki/Q47591","display_name":"Byzantine architecture","level":2,"score":0.4129999876022339},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40610000491142273},{"id":"https://openalex.org/C168021876","wikidata":"https://www.wikidata.org/wiki/Q1353446","display_name":"Byzantine fault tolerance","level":3,"score":0.36390000581741333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3578999936580658},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.34150001406669617},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3353999853134155},{"id":"https://openalex.org/C12590798","wikidata":"https://www.wikidata.org/wiki/Q3933199","display_name":"Replication (statistics)","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.265500009059906},{"id":"https://openalex.org/C101454708","wikidata":"https://www.wikidata.org/wiki/Q17106019","display_name":"Standard Model (mathematical formulation)","level":3,"score":0.2556999921798706},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.11684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11684","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.11684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11684","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.5010771751403809}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,16,118],"Byzantine-resilient":[3],"federated":[4,41,124],"conformal":[5,44,89],"prediction":[6,112],"(FCP)":[7],"method":[8,37,105],"that":[9,30,102],"leverages":[10],"partial":[11,49],"model":[12,19],"sharing,":[13],"where":[14],"only":[15,92],"subset":[17],"of":[18],"parameters":[20],"is":[21],"exchanged":[22],"each":[23],"round.":[24],"Unlike":[25],"existing":[26],"robust":[27,119],"FCP":[28],"approaches":[29],"primarily":[31],"harden":[32],"the":[33,40,53,75,88,103],"calibration":[34,45],"stage,":[35],"our":[36],"protects":[38],"both":[39],"training":[42],"and":[43,56,85,120],"phases.":[46],"During":[47,63],"training,":[48],"sharing":[50],"inherently":[51],"restricts":[52],"attack":[54,99],"surface":[55],"attenuates":[57],"poisoned":[58],"updates":[59],"while":[60],"reducing":[61],"communication.":[62],"calibration,":[64],"clients":[65,80],"compress":[66],"their":[67],"non-conformity":[68],"scores":[69,84],"into":[70],"histogram-based":[71],"characterization":[72],"vectors,":[73],"enabling":[74],"server":[76],"to":[77,86,123],"detect":[78],"Byzantine":[79,98],"via":[81],"distance-based":[82],"maliciousness":[83],"estimate":[87],"quantile":[90],"using":[91],"benign":[93],"contributors.":[94],"Experiments":[95],"across":[96],"diverse":[97],"scenarios":[100],"show":[101],"proposed":[104],"achieves":[106],"closer-to-nominal":[107],"coverage":[108],"with":[109],"substantially":[110],"tighter":[111],"intervals":[113],"than":[114],"standard":[115],"FCP,":[116],"establishing":[117],"communication-efficient":[121],"approach":[122],"uncertainty":[125],"quantification.":[126]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-14T00:00:00"}