{"id":"https://openalex.org/W7170161218","doi":"https://doi.org/10.48550/arxiv.2607.19524","title":"SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training framework","display_name":"SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training framework","publication_year":2026,"publication_date":"2026-07-21","ids":{"openalex":"https://openalex.org/W7170161218","doi":"https://doi.org/10.48550/arxiv.2607.19524"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.19524","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.19524","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.2607.19524","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079252728","display_name":"Akarsh K. Nair","orcid":"https://orcid.org/0000-0002-7734-0367"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nair, Akarsh K","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076670747","display_name":"Muhammad Arifur Rahman","orcid":"https://orcid.org/0000-0002-6774-0041"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rahman, Muhammad Arifur","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007723055","display_name":"Nicholas Shopland","orcid":"https://orcid.org/0000-0003-2082-9070"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shopland, Nicholas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143511175","display_name":"Andy Burton","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Burton, Andy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143523499","display_name":"Jun He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101459434","display_name":"Yi Shen","orcid":"https://orcid.org/0000-0002-2923-7963"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129702067","display_name":"David Baldwin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baldwin, David","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114061946","display_name":"Emma O\u2019Dowd","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"O'Dowd, Emma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037721200","display_name":"Amna Burzi\u0107","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Burzic, Amna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104292588","display_name":"M M Hassan Mahmud","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mahmud, Mufti","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139930696","display_name":"David J. Brown","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Brown, David J.","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":0.4684999883174896,"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":0.4684999883174896,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.4562000036239624,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.009999999776482582,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7609999775886536},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7210999727249146},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5989999771118164},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5440999865531921},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.4505999982357025},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44600000977516174},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.3296000063419342},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.32269999384880066},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.31540000438690186}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7939000129699707},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7609999775886536},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7210999727249146},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5989999771118164},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5440999865531921},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49709999561309814},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4771000146865845},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47049999237060547},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4505999982357025},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44600000977516174},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.32269999384880066},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.2897999882698059},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C3739613","wikidata":"https://www.wikidata.org/wiki/Q679003","display_name":"Distributed Computing Environment","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.25600001215934753},{"id":"https://openalex.org/C2777466982","wikidata":"https://www.wikidata.org/wiki/Q5227287","display_name":"Data extraction","level":3,"score":0.25049999356269836},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.19524","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.19524","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.2607.19524","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.19524","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":{"Federated":[0],"learning":[1],"(FL)":[2],"offers":[3],"a":[4,57,193],"promising":[5],"approach":[6],"to":[7,85,204],"privacy-preserving":[8,79],"clinical":[9,210],"risk":[10,115],"prediction,":[11],"but":[12],"its":[13,44],"deployment":[14],"remains":[15],"limited":[16,51],"by":[17,93],"restricted":[18],"data":[19,37,41,138,201],"sharing,":[20],"client":[21],"heterogeneity,":[22],"class":[23],"imbalance,":[24],"and":[25,102,108,113,126,133,146,155,163,182,195,208],"the":[26,120],"lack":[27],"of":[28],"realistic":[29],"tabular":[30,214],"electronic":[31],"health":[32],"record":[33],"(EHR)":[34],"benchmarks.":[35],"Synthetic":[36],"generation":[38,64],"may":[39],"alleviate":[40],"scarcity,":[42],"yet":[43],"integration":[45],"with":[46,65,152,202],"federated":[47,87,150],"optimisation":[48,95],"has":[49],"received":[50],"systematic":[52],"study.":[53],"We":[54],"propose":[55],"SynPre-FL,":[56],"unified":[58],"framework":[59,197],"combining":[60,199],"high-fidelity":[61],"synthetic":[62,80,121,200],"EHR":[63,215],"synthetic-pretrained":[66],"FL":[67,203],"for":[68,198],"robust":[69,209],"prediction":[70,211],"under":[71,143,169],"non-IID":[72,171],"conditions.":[73],"A":[74],"latent":[75],"autoencoder-diffusion":[76],"model":[77],"generates":[78],"cohorts,":[81],"which":[82],"are":[83],"used":[84],"warm-start":[86],"training.":[88],"This":[89],"pretraining":[90],"is":[91],"followed":[92],"heterogeneity-aware":[94],"using":[96],"class-balanced":[97],"local":[98],"objectives,":[99],"proximal":[100],"regularisation,":[101],"adaptive":[103],"server":[104],"aggregation.":[105],"Post-hoc":[106],"calibration":[107],"federated-safe":[109],"explainability":[110],"support":[111],"reliable":[112],"interpretable":[114],"estimates.":[116],"Experiments":[117],"show":[118],"that":[119],"generator":[122],"preserves":[123],"univariate,":[124],"bivariate,":[125],"multivariate":[127],"structure":[128],"while":[129,177],"protecting":[130],"against":[131],"membership-inference":[132],"reconstruction":[134],"attacks.":[135],"The":[136],"generated":[137],"achieve":[139],"strong":[140],"downstream":[141],"utility":[142],"TSTR,":[144],"TRTS,":[145],"model-based":[147],"evaluations.":[148],"Across":[149],"settings":[151],"5,":[153],"10,":[154],"15":[156],"heterogeneous":[157],"clients,":[158],"SynPre-FL":[159,190],"consistently":[160],"improves":[161,174],"robustness":[162],"scalability":[164],"over":[165],"baseline":[166],"methods,":[167],"especially":[168],"severe":[170],"fragmentation.":[172],"Calibration":[173],"probability":[175],"reliability,":[176],"SHAP":[178],"analysis":[179],"produces":[180],"stable":[181],"clinically":[183],"coherent":[184],"feature":[185],"attributions":[186],"across":[187],"federation":[188],"sizes.":[189],"therefore":[191],"provides":[192],"practical":[194],"reproducible":[196],"enable":[205],"privacy-aware,":[206],"interpretable,":[207],"from":[212],"distributed":[213],"data.":[216]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-24T00:00:00"}
