{"id":"https://openalex.org/W7154201706","doi":"https://doi.org/10.48550/arxiv.2604.11562","title":"The Impact of Federated Learning on Distributed Remote Sensing Archives","display_name":"The Impact of Federated Learning on Distributed Remote Sensing Archives","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154201706","doi":"https://doi.org/10.48550/arxiv.2604.11562"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11562","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.2604.11562","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5095767175","display_name":"Anand Umashankar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Umashankar, Anand","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116820896","display_name":"Karam Tomotaki-Dawoud","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tomotaki-Dawoud, Karam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5112303257","display_name":"Nicolai Schneider","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schneider, Nicolai","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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.4375,"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":0.4375,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.1500999927520752,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.03269999846816063,"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/petabyte","display_name":"Petabyte","score":0.7820000052452087},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5828999876976013},{"id":"https://openalex.org/keywords/earth-observation","display_name":"Earth observation","score":0.5741000175476074},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5669000148773193},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5320000052452087},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4523000121116638},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3953999876976013},{"id":"https://openalex.org/keywords/remote-sensing-application","display_name":"Remote sensing application","score":0.3903999924659729}],"concepts":[{"id":"https://openalex.org/C13600138","wikidata":"https://www.wikidata.org/wiki/Q8799","display_name":"Petabyte","level":3,"score":0.7820000052452087},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7494000196456909},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6262999773025513},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5828999876976013},{"id":"https://openalex.org/C39399123","wikidata":"https://www.wikidata.org/wiki/Q1348989","display_name":"Earth observation","level":3,"score":0.5741000175476074},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5669000148773193},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5320000052452087},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4523000121116638},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.42329999804496765},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3953999876976013},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3917999863624573},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.3903999924659729},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3873000144958496},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.29249998927116394},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27880001068115234},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27489998936653137},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.26989999413490295},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.2597000002861023},{"id":"https://openalex.org/C2987528665","wikidata":"https://www.wikidata.org/wiki/Q854845","display_name":"Earth observation satellite","level":3,"score":0.25619998574256897},{"id":"https://openalex.org/C133199616","wikidata":"https://www.wikidata.org/wiki/Q25386885","display_name":"Empirical modelling","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11562","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.2604.11562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11562","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":{"Remote":[0],"sensing":[1,73,122],"archives":[2],"are":[3],"inherently":[4],"distributed:":[5],"Earth":[6,79],"observation":[7,80],"missions":[8],"such":[9,37],"as":[10],"Sentinel-1,":[11],"Sentinel-2,":[12],"and":[13,25,51,63,112,143,145,161,194],"Sentinel-3":[14],"have":[15],"collectively":[16],"accumulated":[17],"more":[18],"than":[19],"5":[20],"petabytes":[21],"of":[22,78,92,105,138,150,190],"imagery,":[23],"stored":[24],"processed":[26],"across":[27],"many":[28],"geographically":[29],"dispersed":[30],"platforms.":[31],"Training":[32],"machine":[33],"learning":[34,55],"models":[35],"on":[36,165],"data":[38,47,61,180],"in":[39],"a":[40,101],"centralized":[41,185],"fashion":[42],"is":[43,74],"impractical":[44],"due":[45],"to":[46,119],"volume,":[48],"sovereignty":[49],"constraints,":[50],"geographic":[52,87],"distribution.":[53],"Federated":[54],"(FL)":[56],"addresses":[57],"this":[58,97],"by":[59,86],"keeping":[60],"local":[62],"exchanging":[64],"only":[65],"model":[66,153],"updates.":[67],"A":[68],"central":[69],"challenge":[70],"for":[71,176,202],"remote":[72,121],"the":[75,90,147,166,188,198,203],"non-IID":[76,127],"nature":[77],"data:":[81],"label":[82],"distributions":[83],"vary":[84],"strongly":[85],"region,":[88],"degrading":[89],"convergence":[91],"standard":[93],"FL":[94,107],"algorithms.":[95],"In":[96],"paper,":[98],"we":[99],"conduct":[100],"systematic":[102],"empirical":[103],"study":[104],"three":[106,132],"strategies":[108],"--":[109,117],"FedAvg,":[110],"FedProx,":[111],"bulk":[113],"synchronous":[114],"parallel":[115],"(BSP)":[116],"applied":[118],"multi-label":[120,169],"image":[123],"classification":[124],"under":[125,179],"controlled":[126],"label-skew":[128],"conditions.":[129],"We":[130],"evaluate":[131],"convolutional":[133],"neural":[134],"network":[135],"(CNN)":[136],"architectures":[137,178],"increasing":[139],"depth":[140],"(LeNet,":[141],"AlexNet,":[142],"ResNet-34)":[144],"analyze":[146],"joint":[148],"effect":[149],"algorithm":[151],"choice,":[152],"capacity,":[154],"client":[155,157],"fraction,":[156],"count,":[158],"batch":[159],"size,":[160],"communication":[162],"cost.":[163],"Experiments":[164],"UC":[167],"Merced":[168],"dataset":[170,204],"show":[171],"that":[172,182,195],"FedProx":[173],"outperforms":[174],"FedAvg":[175],"deeper":[177],"heterogeneity,":[181],"BSP":[183],"approaches":[184],"accuracy":[186],"at":[187],"cost":[189],"high":[191],"sequential":[192],"communication,":[193],"LeNet":[196],"provides":[197],"best":[199],"accuracy-communication":[200],"trade-off":[201],"scale":[205],"considered.":[206]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-15T00:00:00"}