{"id":"https://openalex.org/W3015541990","doi":"https://doi.org/10.1109/icassp40776.2020.9053733","title":"Federated Classification with Low Complexity Reproducing Kernel Hilbert Space Representations","display_name":"Federated Classification with Low Complexity Reproducing Kernel Hilbert Space Representations","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W3015541990","doi":"https://doi.org/10.1109/icassp40776.2020.9053733","mag":"3015541990"},"language":"en","primary_location":{"id":"doi:10.1109/icassp40776.2020.9053733","doi":"https://doi.org/10.1109/icassp40776.2020.9053733","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053733","pdf_url":null,"source":{"id":"https://openalex.org/S4363607879","display_name":"IEEE International Conference on Acoustics Speech and Signal Processing","issn_l":"1520-6149","issn":["1520-6149","2379-190X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078826130","display_name":"Maria Peifer","orcid":"https://orcid.org/0000-0003-3516-6521","observed_orcids":["https://orcid.org/0000-0003-3516-6521"]},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maria Peifer","raw_affiliation_strings":["Electrical and Systems Engineering, University of Pennsylvania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Systems Engineering, University of Pennsylvania","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078862959","display_name":"Alejandro Ribeiro","orcid":"https://orcid.org/0000-0003-4230-9906","observed_orcids":["https://orcid.org/0000-0003-4230-9906"]},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alejandro Ribeiro","raw_affiliation_strings":["Electrical and Systems Engineering, University of Pennsylvania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Systems Engineering, University of Pennsylvania","institution_ids":["https://openalex.org/I79576946"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I79576946"],"apc_list":null,"apc_paid":null,"fwci":0.0,"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":null,"biblio":{"volume":"20","issue":null,"first_page":"4187","last_page":"4191"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9976000189781189,"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":0.9976000189781189,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.0010000000474974513,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.00039999998989515007,"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/federated-learning","display_name":"federated learning","score":0.9750000238418579},{"id":"https://openalex.org/keywords/reproducing-kernel-hilbert-space","display_name":"reproducing kernel Hilbert space","score":0.921999990940094},{"id":"https://openalex.org/keywords/sample-selection","display_name":"sample selection","score":0.5120000243186951},{"id":"https://openalex.org/keywords/zero-duality-gap","display_name":"zero duality gap","score":0.7850000262260437},{"id":"https://openalex.org/keywords/dual-variables","display_name":"dual variables","score":0.7929999828338623},{"id":"https://openalex.org/keywords/communication-efficiency","display_name":"communication efficiency","score":0.628000020980835},{"id":"https://openalex.org/keywords/non-iid-data","display_name":"non-IID data","score":0.7129999995231628},{"id":"https://openalex.org/keywords/convergence-analysis","display_name":"convergence analysis","score":0.5070000290870667}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7571913599967957},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6649829745292664},{"id":"https://openalex.org/C80884492","wikidata":"https://www.wikidata.org/wiki/Q3345678","display_name":"Reproducing kernel Hilbert space","level":3,"score":0.5358428359031677},{"id":"https://openalex.org/C62799726","wikidata":"https://www.wikidata.org/wiki/Q190056","display_name":"Hilbert space","level":2,"score":0.5205984115600586},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4640023112297058},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.45226070284843445},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4335589110851288},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4154462516307831},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37443768978118896},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18219366669654846},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp40776.2020.9053733","doi":"https://doi.org/10.1109/icassp40776.2020.9053733","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053733","pdf_url":null,"source":{"id":"https://openalex.org/S4363607879","display_name":"IEEE International Conference on Acoustics Speech and Signal Processing","issn_l":"1520-6149","issn":["1520-6149","2379-190X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W134960717","https://openalex.org/W1518544712","https://openalex.org/W1565176583","https://openalex.org/W2009086942","https://openalex.org/W2059173978","https://openalex.org/W2068221105","https://openalex.org/W2107905252","https://openalex.org/W2116612304","https://openalex.org/W2121269968","https://openalex.org/W2131496831","https://openalex.org/W2139269942","https://openalex.org/W2535838896","https://openalex.org/W2561953530","https://openalex.org/W2618494520","https://openalex.org/W2891857734","https://openalex.org/W2912289066","https://openalex.org/W2920125179","https://openalex.org/W2939134861","https://openalex.org/W2943863961","https://openalex.org/W2951792757","https://openalex.org/W2963300197","https://openalex.org/W3098421274","https://openalex.org/W3111293833"],"related_works":["https://openalex.org/W3084246595","https://openalex.org/W7027003133","https://openalex.org/W3131791785","https://openalex.org/W4308216454","https://openalex.org/W7202290174","https://openalex.org/W4302010617","https://openalex.org/W3095173195","https://openalex.org/W4394991826","https://openalex.org/W3111179237","https://openalex.org/W4392904267"],"abstract_inverted_index":{"In":[0,80],"federated":[1,88,187],"learning,":[2],"a":[3,13,85,95,118,198],"centralized":[4,47,78,139,142],"model":[5,66],"is":[6,58,115,130],"realized":[7],"based":[8],"on":[9],"information":[10,71],"received":[11],"from":[12,178],"group":[14],"of":[15,39,185],"agents":[16,27],"each":[17,92,112],"collecting":[18],"data.":[19],"This":[20],"setting":[21],"has":[22,173],"two":[23],"major":[24],"challenges:":[25],"the":[26,43,46,52,56,77,104,109,120,126,138,146,153,157,161,183,194],"observe":[28],"data":[29,41,54],"over":[30,42,55],"different":[31],"distributions":[32],"and":[33,67,108,132,190],"they":[34],"have":[35],"only":[36,134],"limited":[37],"capabilities":[38],"sending":[40,50],"network":[44,57],"to":[45,74,76,137,165,175,193],"unit.":[48,79,140],"Therefore,":[49],"all":[51,176,179],"training":[53],"impractical.":[59],"Each":[60],"agent":[61,93,121],"must":[62],"train":[63],"its":[64],"own":[65],"decide":[68],"what":[69],"relevant":[70],"it":[72,192],"needs":[73],"send":[75],"this":[81],"work":[82],"we":[83],"propose":[84],"method":[86],"for":[87,124],"learning":[89,188],"in":[90],"which":[91,125,172],"learns":[94],"low":[96],"complexity":[97],"Reproducing":[98],"kernel":[99],"Hilbert":[100],"space":[101],"representation.":[102],"Leveraging":[103],"zero":[105,131],"duality":[106],"gap":[107],"fact":[110],"that":[111,151,166],"dual":[113,128],"variable":[114,129],"associated":[116],"with":[117,197],"sample,":[119],"discards":[122],"samples":[123,136,177],"optimal":[127],"sends":[133],"fundamental":[135],"The":[141],"unit":[143,163],"then":[144],"computes":[145],"global":[147],"model.":[148],"We":[149,181],"show":[150],"as":[152],"sample":[154],"size":[155],"grows,":[156],"solution":[158],"obtained":[159,167],"by":[160,168],"central":[162],"converges":[164],"an":[169],"omniscient":[170,195],"classifier":[171,196],"access":[174],"agents.":[180],"illustrate":[182],"performance":[184],"our":[186],"algorithm":[189],"compare":[191],"simulation.":[199]},"counts_by_year":[],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2020-04-17T00:00:00"}
