{"id":"https://openalex.org/W4297841700","doi":"https://doi.org/10.21437/interspeech.2022-10787","title":"Federated Pruning: Improving Neural Network Efficiency with Federated Learning","display_name":"Federated Pruning: Improving Neural Network Efficiency with Federated Learning","publication_year":2022,"publication_date":"2022-09-16","ids":{"openalex":"https://openalex.org/W4297841700","doi":"https://doi.org/10.21437/interspeech.2022-10787"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2022-10787","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2022-10787","pdf_url":null,"source":{"id":"https://openalex.org/S4363604309","display_name":"Interspeech 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"Interspeech 2022","raw_type":"proceedings-article"},"type":"conference-paper","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/A5050956010","display_name":"Rongmei Lin","orcid":"https://orcid.org/0000-0002-3325-5590"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rongmei Lin","raw_affiliation_strings":["Emory University,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Emory University,","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062535903","display_name":"Yonghui Xiao","orcid":"https://orcid.org/0000-0002-9705-3525"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yonghui Xiao","raw_affiliation_strings":["Google LLC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google LLC","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058307363","display_name":"Tien-Ju Yang","orcid":"https://orcid.org/0000-0003-4728-0321"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tien-Ju Yang","raw_affiliation_strings":["Google LLC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google LLC","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037644321","display_name":"Ding Zhao","orcid":"https://orcid.org/0000-0002-9400-8446"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ding Zhao","raw_affiliation_strings":["Google LLC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google LLC","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078394535","display_name":"Li Xiong","orcid":"https://orcid.org/0000-0001-7354-0428"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li Xiong","raw_affiliation_strings":["Emory University,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Emory University,","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104003387","display_name":"Giovanni Motta","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Giovanni Motta","raw_affiliation_strings":["Google LLC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google LLC","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110711540","display_name":"Fran\u00e7oise Beaufays","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Francoise Beaufays","raw_affiliation_strings":["Google LLC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google LLC","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3495,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.835037,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1701","last_page":"1705"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9991000294685364,"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":0.9991000294685364,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9987999796867371,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/pruning","display_name":"Pruning","score":0.829635739326477},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8217593431472778},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6210429668426514},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5726922154426575},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48610997200012207},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.3239207863807678}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.829635739326477},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8217593431472778},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6210429668426514},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5726922154426575},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48610997200012207},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3239207863807678},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21437/interspeech.2022-10787","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2022-10787","pdf_url":null,"source":{"id":"https://openalex.org/S4363604309","display_name":"Interspeech 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"Interspeech 2022","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.5600000023841858}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1494198834","https://openalex.org/W2554302513","https://openalex.org/W2805003733","https://openalex.org/W2905741102","https://openalex.org/W2936774411","https://openalex.org/W2949413532","https://openalex.org/W3008181812","https://openalex.org/W3094502228","https://openalex.org/W3095311338","https://openalex.org/W3097714942","https://openalex.org/W3097777922","https://openalex.org/W3101584733","https://openalex.org/W3160766462","https://openalex.org/W3162309234","https://openalex.org/W3204052447","https://openalex.org/W4286908227","https://openalex.org/W4288101682","https://openalex.org/W4294106961","https://openalex.org/W4297813615","https://openalex.org/W4297853509","https://openalex.org/W4299283926","https://openalex.org/W4302343710","https://openalex.org/W4318619660"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Automatic":[0],"Speech":[1],"Recognition":[2],"models":[3],"require":[4],"large":[5,66],"amount":[6,95],"of":[7,15,96,120,123],"speech":[8],"data":[9,17,47,98],"for":[10,65],"training,":[11],"and":[12,28,56,116],"the":[13,46,53,81,90,93,105,121],"collection":[14],"such":[16,69],"often":[18],"leads":[19],"to":[20,31,75,89,103,109],"privacy":[21],"concerns.Federated":[22],"learning":[23,39],"has":[24],"been":[25],"widely":[26],"used":[27],"is":[29],"considered":[30],"be":[32,101],"an":[33],"effective":[34],"decentralized":[35],"technique":[36],"by":[37],"collaboratively":[38],"a":[40,77],"shared":[41],"prediction":[42],"model":[43,79],"while":[44,84],"keeping":[45],"local":[48],"on":[49,59],"different":[50,113],"clients":[51,60,97],"devices.However,":[52],"limited":[54],"computation":[55],"communication":[57],"resources":[58],"devices":[61],"present":[62],"practical":[63],"difficulties":[64],"models.To":[67],"overcome":[68],"challenges,":[70],"we":[71],"propose":[72],"Federated":[73],"Pruning":[74],"train":[76],"reduced":[78],"under":[80],"federated":[82],"setting,":[83],"maintaining":[85],"similar":[86],"performance":[87],"compared":[88,108],"full":[91],"model.Moreover,":[92],"vast":[94],"can":[99],"also":[100],"leveraged":[102],"improve":[104],"pruning":[106,114],"results":[107],"centralized":[110],"training.We":[111],"explore":[112],"schemes":[115],"provide":[117],"empirical":[118],"evidence":[119],"effectiveness":[122],"our":[124],"methods.":[125]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}