{"id":"https://openalex.org/W7147189080","doi":"https://doi.org/10.48550/arxiv.2603.29633","title":"Self-Supervised Federated Learning under Data Heterogeneity for Label-Scarce Diatom Classification","display_name":"Self-Supervised Federated Learning under Data Heterogeneity for Label-Scarce Diatom Classification","publication_year":2026,"publication_date":"2026-03-31","ids":{"openalex":"https://openalex.org/W7147189080","doi":"https://doi.org/10.48550/arxiv.2603.29633"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.29633","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29633","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.2603.29633","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079332435","display_name":"Mingkun Tan","orcid":"https://orcid.org/0000-0002-5997-9087"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Mingkun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132655564","display_name":"Xilu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xilu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057316063","display_name":"Michael Kloster","orcid":"https://orcid.org/0000-0001-9244-4925"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kloster, Michael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5033485866","display_name":"Tim W. Nattkemper","orcid":"https://orcid.org/0000-0002-7986-1158"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nattkemper, Tim W.","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/T12697","display_name":"Water Quality Monitoring Technologies","score":0.1265999972820282,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12697","display_name":"Water Quality Monitoring Technologies","score":0.1265999972820282,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12012","display_name":"Diatoms and Algae Research","score":0.10300000011920929,"subfield":{"id":"https://openalex.org/subfields/2502","display_name":"Biomaterials"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14249","display_name":"Water Quality Monitoring and Analysis","score":0.05790000036358833,"subfield":{"id":"https://openalex.org/subfields/2311","display_name":"Waste Management and Disposal"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/homogeneous","display_name":"Homogeneous","score":0.567799985408783},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.5248000025749207},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4997999966144562},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4968999922275543},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.42829999327659607},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.3582000136375427},{"id":"https://openalex.org/keywords/data-type","display_name":"Data type","score":0.34880000352859497},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.34769999980926514},{"id":"https://openalex.org/keywords/semantic-heterogeneity","display_name":"Semantic heterogeneity","score":0.34610000252723694}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7437999844551086},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.567799985408783},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.5248000025749207},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5216000080108643},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4997999966144562},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4968999922275543},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.42829999327659607},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39070001244544983},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3880999982357025},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.3582000136375427},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.34880000352859497},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.34769999980926514},{"id":"https://openalex.org/C2778180026","wikidata":"https://www.wikidata.org/wiki/Q18378163","display_name":"Semantic heterogeneity","level":4,"score":0.34610000252723694},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.3215000033378601},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C13460635","wikidata":"https://www.wikidata.org/wiki/Q85753676","display_name":"Classification scheme","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C180478619","wikidata":"https://www.wikidata.org/wiki/Q7574066","display_name":"Spatial heterogeneity","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C2780267512","wikidata":"https://www.wikidata.org/wiki/Q6997828","display_name":"Nestedness","level":3,"score":0.28299999237060547},{"id":"https://openalex.org/C82578977","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Data aggregator","level":3,"score":0.28119999170303345},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C13670688","wikidata":"https://www.wikidata.org/wiki/Q3500548","display_name":"Space partitioning","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C75235859","wikidata":"https://www.wikidata.org/wiki/Q582659","display_name":"Exponential growth","level":2,"score":0.2549000084400177},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.29633","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29633","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.2603.29633","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29633","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":{"Label-scarce":[0],"visual":[1],"classification":[2,73],"under":[3,167,187],"decentralized":[4,221],"and":[5,43,79,95,126,170,193],"heterogeneous":[6,171],"data":[7,38,56,83,91,178],"is":[8,180],"a":[9,29,75,106,112,196,213],"fundamental":[10],"challenge":[11],"in":[12,89,105,155,176,220],"pattern":[13,40],"recognition,":[14],"especially":[15],"when":[16],"sites":[17],"exhibit":[18],"partially":[19,54],"overlapping":[20],"class":[21,124],"sets.":[22],"While":[23],"self-supervised":[24],"federated":[25],"learning":[26],"(SSFL)":[27],"offers":[28],"promising":[30],"solution,":[31],"existing":[32],"studies":[33],"commonly":[34],"assume":[35],"the":[36,103,138],"same":[37],"heterogeneity":[39,118,175,219],"throughout":[41],"pre-training":[42,94],"fine-tuning.":[44,100],"Moreover,":[45],"current":[46],"partitioning":[47,113],"schemes":[48],"often":[49],"fail":[50],"to":[51,150],"generate":[52],"pure":[53],"class-disjoint":[55],"settings,":[57],"limiting":[58],"controllable":[59,107],"simulation":[60],"of":[61,133],"real-world":[62,77],"label-space":[63,96,117,188,218],"heterogeneity.":[64,84],"In":[65],"this":[66,202],"work,":[67],"we":[68,109,141],"introduce":[69],"SSFL":[70,162],"for":[71,216],"diatom":[72],"as":[74,207],"representative":[76],"instance":[78],"systematically":[80],"investigate":[81],"stage-specific":[82],"We":[85],"study":[86,102],"cross-site":[87],"variation":[88],"unlabeled":[90,177],"volume":[92,179],"during":[93,98],"misalignment":[97],"downstream":[99],"To":[101],"latter":[104],"setting,":[108],"propose":[110,143],"PreDi,":[111],"scheme":[114],"that":[115,161],"disentangles":[116],"into":[119],"two":[120],"orthogonal":[121],"dimensions,":[122],"namely":[123],"Prevalence":[125],"class-set":[127],"size":[128],"Disparity,":[129],"enabling":[130],"separate":[131],"analysis":[132],"their":[134],"effects.":[135],"Guided":[136],"by":[137],"resulting":[139],"insights,":[140],"further":[142],"PreP-WFL":[144,199],"(Prevalence-based":[145],"Personalized":[146],"Weighted":[147],"Federated":[148],"Learning)":[149],"adaptively":[151],"strengthen":[152],"rare-class":[153],"representations":[154],"low-prevalence":[156],"scenarios.":[157],"Extensive":[158],"experiments":[159],"show":[160],"consistently":[163],"outperforms":[164],"local-only":[165],"training":[166],"both":[168],"homogeneous":[169],"settings.":[172],"The":[173],"pronounced":[174],"associated":[181],"with":[182,204],"improved":[183],"representation":[184],"pre-training,":[185],"whereas":[186],"heterogeneity,":[189],"prevalence":[190,208],"dominates":[191],"performance":[192],"disparity":[194],"has":[195],"smaller":[197],"effect.":[198],"effectively":[200],"mitigates":[201],"degradation,":[203],"gains":[205],"increasing":[206],"decreases.":[209],"These":[210],"findings":[211],"provide":[212],"mechanistic":[214],"basis":[215],"characterizing":[217],"recognition":[222],"systems.":[223]},"counts_by_year":[],"updated_date":"2026-08-18T07:49:30.821534","created_date":"2026-04-02T00:00:00"}
