{"id":"https://openalex.org/W4399597630","doi":"https://doi.org/10.48550/arxiv.2406.06755","title":"Optimal Federated Learning for Nonparametric Regression with\n Heterogeneous Distributed Differential Privacy Constraints","display_name":"Optimal Federated Learning for Nonparametric Regression with\n Heterogeneous Distributed Differential Privacy Constraints","publication_year":2024,"publication_date":"2024-06-10","ids":{"openalex":"https://openalex.org/W4399597630","doi":"https://doi.org/10.48550/arxiv.2406.06755"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2406.06755","pdf_url":"http://arxiv.org/pdf/2406.06755","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://arxiv.org/pdf/2406.06755","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5105471967","display_name":"Tommaso Cai","orcid":"https://orcid.org/0000-0002-7234-3526"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, T. Tony","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001622508","display_name":"Abhinav Chakraborty","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chakraborty, Abhinav","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5058641566","display_name":"Lasse Vuursteen","orcid":"https://orcid.org/0000-0002-3255-8549"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vuursteen, Lasse","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"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":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":77},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9997,"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":0.9997,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9902,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9882,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/differential-privacy","display_name":"Differential Privacy","score":0.8907949},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated Learning","score":0.57403266}],"concepts":[{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.8907949},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.6727885},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.66087234},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.6138932},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.577096},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.57403266},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.49201056},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.40509176},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27001756},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24556899},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.23299506},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.22241631},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17848441},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14005563},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07188457},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2406.06755","pdf_url":"http://arxiv.org/pdf/2406.06755","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2406.06755","pdf_url":"http://arxiv.org/pdf/2406.06755","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4322580403","https://openalex.org/W4321612632","https://openalex.org/W4286971788","https://openalex.org/W4280591108","https://openalex.org/W3199340467","https://openalex.org/W3193217249","https://openalex.org/W3157608626","https://openalex.org/W3132132958","https://openalex.org/W2184572292","https://openalex.org/W1541412963"],"abstract_inverted_index":{"This":[0,134],"paper":[1],"studies":[2],"federated":[3],"learning":[4],"for":[5,84],"nonparametric":[6],"regression":[7],"in":[8,112,147],"the":[9,57,96,108,115,121,128,137,152],"context":[10],"of":[11,54,114],"distributed":[12,160],"samples":[13],"across":[14,39],"different":[15],"servers,":[16],"each":[17],"adhering":[18],"to":[19,78,144],"distinct":[20],"differential":[21,36],"privacy":[22,37,102,116,129,146,161],"constraints.":[23,162],"The":[24],"setting":[25],"we":[26,106],"consider":[27],"is":[28,142],"heterogeneous,":[29],"encompassing":[30],"both":[31,44,85],"varying":[32],"sample":[33],"sizes":[34],"and":[35,46,51,67,87,101,150,156],"constraints":[38],"servers.":[40],"Within":[41],"this":[42],"framework,":[43],"global":[45,86,157],"pointwise":[47,88,155],"estimation":[48,158],"are":[49,60,65,71,82],"considered,":[50],"optimal":[52],"rates":[53],"convergence":[55],"over":[56],"Besov":[58],"spaces":[59],"established.":[61],"Distributed":[62],"privacy-preserving":[63],"estimators":[64],"proposed":[66],"their":[68],"risk":[69],"properties":[70],"investigated.":[72],"Matching":[73],"minimax":[74],"lower":[75],"bounds,":[76],"up":[77],"a":[79,132],"logarithmic":[80],"factor,":[81],"established":[83],"estimation.":[89],"Together,":[90],"these":[91],"findings":[92],"shed":[93],"light":[94],"on":[95],"tradeoff":[97],"between":[98,154],"statistical":[99],"accuracy":[100],"preservation.":[103],"In":[104],"particular,":[105],"characterize":[107],"compromise":[109],"not":[110],"only":[111],"terms":[113],"budget":[117],"but":[118],"also":[119],"concerning":[120],"loss":[122],"incurred":[123],"by":[124],"distributing":[125],"data":[126],"within":[127],"framework":[130],"as":[131],"whole.":[133],"insight":[135],"captures":[136],"folklore":[138],"wisdom":[139],"that":[140],"it":[141],"easier":[143],"retain":[145],"larger":[148],"samples,":[149],"explores":[151],"differences":[153],"under":[159]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4399597630","counts_by_year":[],"updated_date":"2025-04-16T16:22:01.375706","created_date":"2024-06-13"}