{"id":"https://openalex.org/W3170490848","doi":"https://doi.org/10.1109/icme51207.2021.9428075","title":"Fedns: Improving Federated Learning for Collaborative Image Classification on Mobile Clients","display_name":"Fedns: Improving Federated Learning for Collaborative Image Classification on Mobile Clients","publication_year":2021,"publication_date":"2021-06-09","ids":{"openalex":"https://openalex.org/W3170490848","doi":"https://doi.org/10.1109/icme51207.2021.9428075","mag":"3170490848"},"language":"en","primary_location":{"id":"doi:10.1109/icme51207.2021.9428075","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme51207.2021.9428075","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Multimedia and Expo (ICME)","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/A5070252027","display_name":"Yaoxin Zhuo","orcid":"https://orcid.org/0000-0001-8166-4260"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yaoxin Zhuo","raw_affiliation_strings":["Arizona State University"],"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032615847","display_name":"Baoxin Li","orcid":"https://orcid.org/0000-0002-9294-4572"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Baoxin Li","raw_affiliation_strings":["Arizona State University"],"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5070252027"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":1.2237,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.83081123,"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":1.0,"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":1.0,"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/T10237","display_name":"Cryptography and Data Security","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":0.9883999824523926,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7557616233825684},{"id":"https://openalex.org/keywords/collaborative-learning","display_name":"Collaborative learning","score":0.4744243025779724},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42498916387557983},{"id":"https://openalex.org/keywords/mobile-computing","display_name":"Mobile computing","score":0.421176552772522},{"id":"https://openalex.org/keywords/collaborative-software","display_name":"Collaborative software","score":0.4149715304374695},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3833175301551819},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.35038143396377563},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.29366475343704224},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27533918619155884},{"id":"https://openalex.org/keywords/knowledge-management","display_name":"Knowledge management","score":0.2713448405265808},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.11950775980949402}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7557616233825684},{"id":"https://openalex.org/C138020889","wikidata":"https://www.wikidata.org/wiki/Q2349659","display_name":"Collaborative learning","level":2,"score":0.4744243025779724},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42498916387557983},{"id":"https://openalex.org/C144543869","wikidata":"https://www.wikidata.org/wiki/Q2738570","display_name":"Mobile computing","level":2,"score":0.421176552772522},{"id":"https://openalex.org/C554579003","wikidata":"https://www.wikidata.org/wiki/Q474157","display_name":"Collaborative software","level":2,"score":0.4149715304374695},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3833175301551819},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.35038143396377563},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.29366475343704224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27533918619155884},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.2713448405265808},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.11950775980949402}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme51207.2021.9428075","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme51207.2021.9428075","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2535838896","https://openalex.org/W2750384547","https://openalex.org/W2769644379","https://openalex.org/W2798720628","https://openalex.org/W2807006176","https://openalex.org/W2970825779","https://openalex.org/W2977072935","https://openalex.org/W2982464076","https://openalex.org/W2999593354","https://openalex.org/W3006017224","https://openalex.org/W3021654819","https://openalex.org/W3035046187","https://openalex.org/W3105122387","https://openalex.org/W3106673115","https://openalex.org/W3118608800","https://openalex.org/W3159623990","https://openalex.org/W3184192107","https://openalex.org/W4294106961","https://openalex.org/W4297629726","https://openalex.org/W4297687186","https://openalex.org/W4318619660","https://openalex.org/W6684191040","https://openalex.org/W6728757088","https://openalex.org/W6743688258","https://openalex.org/W6746200960","https://openalex.org/W6752029299","https://openalex.org/W6763181277","https://openalex.org/W6771536673","https://openalex.org/W6772307254","https://openalex.org/W6779308105","https://openalex.org/W6779888419","https://openalex.org/W6785597877","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2555523046","https://openalex.org/W82744102","https://openalex.org/W4254957076","https://openalex.org/W583243736","https://openalex.org/W4247018887","https://openalex.org/W14588676","https://openalex.org/W2588298871","https://openalex.org/W1973649246","https://openalex.org/W32651165","https://openalex.org/W4255161829"],"abstract_inverted_index":{"Federated":[0,32,67],"Learning":[1],"(FL)":[2],"is":[3,31,36],"a":[4,15,23,63,95],"paradigm":[5],"that":[6,124],"aims":[7],"to":[8,94],"support":[9],"loosely":[10],"connected":[11],"clients":[12],"in":[13,77],"learning":[14],"global":[16,74,98],"model":[17,75,99],"collaboratively":[18],"with":[19,46,117],"the":[20,43,47,56,72,78,85,89,102,106],"help":[21],"of":[22,42,105],"centralized":[24],"server.":[25],"The":[26],"most":[27],"popular":[28],"FL":[29,79],"algorithm":[30],"Averaging":[33],"(FedAvg),":[34],"which":[35],"based":[37,51],"on":[38,52],"taking":[39],"weighted":[40],"average":[41],"client":[44],"models,":[45],"weights":[48],"determined":[49],"largely":[50],"dataset":[53],"sizes":[54],"at":[55,88],"clients.":[57,107],"In":[58],"this":[59],"paper,":[60],"we":[61,115],"propose":[62],"new":[64],"approach,":[65],"termed":[66],"Node":[68],"Selection":[69],"(FedNS),":[70],"for":[71],"server\u2019s":[73],"aggregation":[76],"setting.":[80],"FedNS":[81,125],"filters":[82],"and":[83,122],"re-weights":[84],"clients\u2019":[86],"models":[87],"node/kernel":[90],"level,":[91],"hence":[92],"leading":[93],"potentially":[96],"better":[97],"by":[100],"fusing":[101],"best":[103],"components":[104],"Using":[108],"collaborative":[109],"image":[110],"classification":[111],"as":[112],"an":[113],"example,":[114],"show":[116],"experiments":[118],"from":[119],"multiple":[120],"datasets":[121],"networks":[123],"can":[126],"consistently":[127],"achieve":[128],"improved":[129],"performance":[130],"over":[131],"FedAvg.":[132]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}