{"id":"https://openalex.org/W7134815119","doi":"https://doi.org/10.48550/arxiv.2603.07468","title":"FedEU: Evidential Uncertainty-Driven Federated Fine-Tuning of Vision Foundation Models for Remote Sensing Image Segmentation","display_name":"FedEU: Evidential Uncertainty-Driven Federated Fine-Tuning of Vision Foundation Models for Remote Sensing Image Segmentation","publication_year":2026,"publication_date":"2026-03-08","ids":{"openalex":"https://openalex.org/W7134815119","doi":"https://doi.org/10.48550/arxiv.2603.07468"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.07468","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128650068","display_name":"Xiaokang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xiaokang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128672704","display_name":"Xuran Xiong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Xuran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113434088","display_name":"Jianzhong Huang","orcid":"https://orcid.org/0009-0003-7081-1765"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Jianzhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128688485","display_name":"Lefei Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Lefei","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3032226,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.31139999628067017,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.31139999628067017,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.22360000014305115,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.09600000083446503,"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/feature","display_name":"Feature (linguistics)","score":0.5875999927520752},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5570999979972839},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5184999704360962},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4885999858379364},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.4869000017642975},{"id":"https://openalex.org/keywords/upload","display_name":"Upload","score":0.47609999775886536},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4740999937057495},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.46380001306533813},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4440000057220459},{"id":"https://openalex.org/keywords/remote-sensing-application","display_name":"Remote sensing application","score":0.4000999927520752}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7782999873161316},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5875999927520752},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5570999979972839},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5184999704360962},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5037000179290771},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4885999858379364},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4869000017642975},{"id":"https://openalex.org/C71901391","wikidata":"https://www.wikidata.org/wiki/Q7126699","display_name":"Upload","level":2,"score":0.47609999775886536},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4740999937057495},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.46380001306533813},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4440000057220459},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4260999858379364},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.4000999927520752},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.3815999925136566},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.37209999561309814},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36230000853538513},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.350600004196167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34630000591278076},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.3422999978065491},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.31690001487731934},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3046000003814697},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2718000113964081},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.263700008392334},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.25459998846054077}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.07468","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.07468","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.07468","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.07468","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals","score":0.45941951870918274}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Remote":[0],"sensing":[1],"image":[2],"segmentation":[3],"(RSIS)":[4],"in":[5,212],"federated":[6,94,217],"environments":[7],"has":[8],"gained":[9],"increasing":[10],"attention":[11,146],"because":[12],"it":[13],"enables":[14,199],"collaborative":[15,73],"model":[16,201],"training":[17],"across":[18,203],"distributed":[19],"datasets":[20,189],"without":[21],"sharing":[22],"raw":[23],"imagery":[24],"or":[25],"annotations.":[26],"Federated":[27],"RSIS":[28,99],"combined":[29],"with":[30,46],"parameter-efficient":[31],"fine-tuning":[32,98],"(PEFT)":[33],"can":[34],"unleash":[35],"the":[36,54,70,77,128,159,175,191],"generalization":[37],"power":[38],"of":[39,57,72,79,116,177,194],"pretrained":[40,58],"foundation":[41],"models":[42,59,100,118],"for":[43,82,97],"real-world":[44],"applications,":[45],"minimal":[47],"parameter":[48,150],"aggregation":[49,165],"and":[50,68,119,147,180,215],"communication":[51],"overhead.":[52],"However,":[53],"dynamic":[55],"adaptation":[56,202],"to":[60,76,112,135,158,161],"heterogeneous":[61,188],"client":[62],"data":[63,125],"inevitably":[64],"increases":[65],"update":[66,151],"uncertainty":[67,80,108,154],"compromises":[69],"reliability":[71],"optimization":[74,95],"due":[75],"lack":[78],"estimation":[81],"each":[83],"local":[84,117,124],"model.":[85],"To":[86],"bridge":[87],"this":[88],"gap,":[89],"we":[90],"present":[91],"FedEU,":[92],"a":[93,167],"framework":[96],"driven":[101],"by":[102,206],"evidential":[103,107],"uncertainty.":[104],"Specifically,":[105],"personalized":[106,145],"modeling":[109],"is":[110,133],"introduced":[111],"quantify":[113],"epistemic":[114],"variations":[115],"identify":[120],"high-risk":[121],"areas":[122],"under":[123],"distributions.":[126],"Furthermore,":[127],"client-specific":[129,142],"feature":[130,138],"embedding":[131],"(CFE)":[132],"exploited":[134],"enhance":[136],"channel-aware":[137],"representation":[139],"while":[140],"preserving":[141],"properties":[143],"through":[144],"an":[148],"element-aware":[149],"approach.":[152],"These":[153],"estimates":[155],"are":[156],"uploaded":[157],"server":[160],"enable":[162],"adaptive":[163],"global":[164],"via":[166],"Top-k":[168],"uncertainty-guided":[169],"weighting":[170],"(TUW)":[171],"strategy,":[172],"which":[173],"mitigates":[174],"impact":[176],"distribution":[178],"shifts":[179],"unreliable":[181],"updates.":[182],"Extensive":[183],"experiments":[184],"on":[185],"three":[186],"large-scale":[187],"demonstrate":[190],"superior":[192],"performance":[193],"FedEU.":[195],"More":[196],"importantly,":[197],"FedEU":[198],"balanced":[200],"diverse":[204],"clients":[205],"explicitly":[207],"reducing":[208],"prediction":[209],"uncertainty,":[210],"resulting":[211],"more":[213],"robust":[214],"reliable":[216],"outcomes.":[218],"The":[219],"source":[220],"codes":[221],"will":[222],"be":[223],"available":[224],"at":[225],"https://github.com/zxk688/FedEU.":[226]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2026-03-11T00:00:00"}
