{"id":"https://openalex.org/W4376480773","doi":"https://doi.org/10.1109/wcnc55385.2023.10118879","title":"Communication Efficient Federated Learning via Channel-wise Dynamic Pruning","display_name":"Communication Efficient Federated Learning via Channel-wise Dynamic Pruning","publication_year":2023,"publication_date":"2023-03-01","ids":{"openalex":"https://openalex.org/W4376480773","doi":"https://doi.org/10.1109/wcnc55385.2023.10118879"},"language":"en","primary_location":{"id":"doi:10.1109/wcnc55385.2023.10118879","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc55385.2023.10118879","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","raw_type":"proceedings-article"},"type":"article","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/A5035197459","display_name":"Bo Tao","orcid":"https://orcid.org/0000-0002-5886-1992"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Bo Tao","raw_affiliation_strings":["East China Normal University,School of Data Science and Engineering,Shanghai,China","School of Data Science and Engineering, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"East China Normal University,School of Data Science and Engineering,Shanghai,China","institution_ids":["https://openalex.org/I66867065"]},{"raw_affiliation_string":"School of Data Science and Engineering, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114734448","display_name":"Cen Chen","orcid":"https://orcid.org/0000-0003-0325-1705"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cen Chen","raw_affiliation_strings":["East China Normal University,School of Data Science and Engineering,Shanghai,China","School of Data Science and Engineering, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"East China Normal University,School of Data Science and Engineering,Shanghai,China","institution_ids":["https://openalex.org/I66867065"]},{"raw_affiliation_string":"School of Data Science and Engineering, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100356345","display_name":"Huimin Chen","orcid":"https://orcid.org/0000-0003-4483-1140"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huimin Chen","raw_affiliation_strings":["East China Normal University,School of Data Science and Engineering,Shanghai,China","School of Data Science and Engineering, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"East China Normal University,School of Data Science and Engineering,Shanghai,China","institution_ids":["https://openalex.org/I66867065"]},{"raw_affiliation_string":"School of Data Science and Engineering, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5035197459"],"corresponding_institution_ids":["https://openalex.org/I66867065"],"apc_list":null,"apc_paid":null,"fwci":1.2238,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.82753693,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"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":0.9998999834060669,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9907000064849854,"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/T10964","display_name":"Wireless Communication Security Techniques","score":0.9800999760627747,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8777257204055786},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.7463903427124023},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6901005506515503},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6413085460662842},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5409579873085022},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5389206409454346},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5210527777671814},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5183397531509399},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.45957040786743164},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4498525857925415},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.42379891872406006},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.326870858669281},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.16032367944717407}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8777257204055786},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7463903427124023},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6901005506515503},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6413085460662842},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5409579873085022},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5389206409454346},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5210527777671814},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5183397531509399},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.45957040786743164},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4498525857925415},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.42379891872406006},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.326870858669281},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.16032367944717407},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcnc55385.2023.10118879","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc55385.2023.10118879","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1583837637","https://openalex.org/W1821462560","https://openalex.org/W2112796928","https://openalex.org/W2530417694","https://openalex.org/W2553910756","https://openalex.org/W2752782242","https://openalex.org/W2783873922","https://openalex.org/W2903471046","https://openalex.org/W2947963429","https://openalex.org/W2948116732","https://openalex.org/W2971275988","https://openalex.org/W3090393599","https://openalex.org/W3100837220","https://openalex.org/W3103034221","https://openalex.org/W3119997217","https://openalex.org/W3162927991","https://openalex.org/W3166954785","https://openalex.org/W3185811833","https://openalex.org/W3187127611","https://openalex.org/W3201030831","https://openalex.org/W3203587904","https://openalex.org/W3205956402","https://openalex.org/W3214603841","https://openalex.org/W4221148876","https://openalex.org/W4286892895","https://openalex.org/W4287906413","https://openalex.org/W4300427714","https://openalex.org/W4318619660","https://openalex.org/W6637373629","https://openalex.org/W6638523607","https://openalex.org/W6728757088","https://openalex.org/W6729763630","https://openalex.org/W6738383168","https://openalex.org/W6748171661","https://openalex.org/W6757139170","https://openalex.org/W6767534822","https://openalex.org/W6772318479","https://openalex.org/W6783533280","https://openalex.org/W6786597537","https://openalex.org/W6796484261","https://openalex.org/W6796536798","https://openalex.org/W6796978233","https://openalex.org/W6798249133","https://openalex.org/W6804052817","https://openalex.org/W6809453916"],"related_works":["https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W2140186469","https://openalex.org/W4389724018","https://openalex.org/W4318719684","https://openalex.org/W4318559728","https://openalex.org/W2775233965","https://openalex.org/W4360995913","https://openalex.org/W4312193868","https://openalex.org/W4308165509"],"abstract_inverted_index":{"Federated":[0,90,114],"Learning":[1,91],"(FL)":[2],"received":[3],"widespread":[4],"attention":[5],"in":[6,31,48,53,113,129],"5G":[7],"mobile":[8],"edge":[9],"networks":[10],"(MENs)":[11],"as":[12],"it":[13],"enables":[14],"collaborative":[15],"training":[16,74],"deep":[17],"learning":[18,34],"models":[19],"without":[20],"disclosing":[21],"users'":[22],"private":[23],"data.":[24],"As":[25],"the":[26,32,97,110,119,130],"increasing":[27],"number":[28],"of":[29],"parameters":[30,112],"machine":[33],"model":[35,50,59,127],"poses":[36],"a":[37,45,63,81,102],"tremendous":[38],"challenge":[39],"for":[40,87],"resource-constrained":[41],"devices,":[42],"there":[43],"is":[44],"growing":[46],"interest":[47],"applying":[49],"compression":[51,60],"methods":[52,61],"federated":[54],"learning.":[55],"However,":[56],"most":[57],"existing":[58],"require":[62],"cumbersome":[64],"procedure":[65],"that":[66],"introduces":[67],"many":[68],"additional":[69],"hyperparameters":[70],"and":[71,107,126],"much":[72],"more":[73],"time.":[75],"In":[76],"this":[77],"paper,":[78],"we":[79],"propose":[80],"novel":[82],"Channel-wise":[83],"Dynamic":[84],"Pruning":[85],"method":[86,121],"communication":[88,124],"efficient":[89],"(FedCDP).":[92],"The":[93,133],"scheme":[94],"dynamically":[95],"evaluates":[96],"channel-wise":[98],"parameter":[99],"importance":[100],"via":[101],"fast":[103],"Taylor":[104],"series":[105],"evaluation":[106],"only":[108],"communicates":[109],"important":[111],"Learning.":[115],"Extensive":[116],"experiments":[117],"show":[118],"proposed":[120],"achieves":[122],"both":[123],"efficiency":[125],"effectiveness":[128],"benchmark":[131],"datasets.":[132],"source":[134],"codes":[135],"are":[136],"available":[137],"at":[138],"https://github.com/tabo0/FedCDP.":[139]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}