{"id":"https://openalex.org/W3045889854","doi":"https://doi.org/10.1109/icc40277.2020.9148586","title":"Dynamic Sample Selection for Federated Learning with Heterogeneous Data in Fog Computing","display_name":"Dynamic Sample Selection for Federated Learning with Heterogeneous Data in Fog Computing","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3045889854","doi":"https://doi.org/10.1109/icc40277.2020.9148586","mag":"3045889854"},"language":"en","primary_location":{"id":"doi:10.1109/icc40277.2020.9148586","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc40277.2020.9148586","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","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/A5000130332","display_name":"Lingshuang Cai","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lingshuang Cai","raw_affiliation_strings":["Department of Information and Software Engineering, University of Electronic Science and Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Department of Information and Software Engineering, University of Electronic Science and Technology, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039060932","display_name":"Di Lin","orcid":"https://orcid.org/0000-0002-6904-3370"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Lin","raw_affiliation_strings":["Department of Information and Software Engineering, University of Electronic Science and Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Department of Information and Software Engineering, University of Electronic Science and Technology, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100744176","display_name":"Jiale Zhang","orcid":"https://orcid.org/0000-0002-2143-5666"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiale Zhang","raw_affiliation_strings":["Department of Computer Science and Technology, Nanjin University of Aeronautics and Astronautics, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Nanjin University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005228053","display_name":"Shui Yu","orcid":"https://orcid.org/0000-0003-4485-6743"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shui Yu","raw_affiliation_strings":["Department of Engineering and Information Technology, University of Technology Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Engineering and Information Technology, University of Technology Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5000130332"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":3.8056,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":{"value":0.94547799,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9884999990463257,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.968999981880188,"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/computer-science","display_name":"Computer science","score":0.8460544943809509},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.7008489966392517},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.6012309789657593},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5929960012435913},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5032631754875183},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.47501644492149353},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.44808945059776306},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4116029739379883},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.38082101941108704},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3686280846595764},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3561002314090729}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8460544943809509},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.7008489966392517},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6012309789657593},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5929960012435913},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5032631754875183},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.47501644492149353},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.44808945059776306},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4116029739379883},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.38082101941108704},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3686280846595764},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3561002314090729},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","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/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icc40277.2020.9148586","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc40277.2020.9148586","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","raw_type":"proceedings-article"},{"id":"pmh:oai:opus.lib.uts.edu.au:10453/148111","is_oa":false,"landing_page_url":"http://hdl.handle.net/10453/148111","pdf_url":null,"source":{"id":"https://openalex.org/S4306401357","display_name":"UTS ePRESS (University of Technology Sydney)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114017466","host_organization_name":"University of Technology Sydney","host_organization_lineage":["https://openalex.org/I114017466"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2000042664","https://openalex.org/W2061570747","https://openalex.org/W2065842544","https://openalex.org/W2104933073","https://openalex.org/W2279597087","https://openalex.org/W2535838896","https://openalex.org/W2604442880","https://openalex.org/W2798720628","https://openalex.org/W2950865323","https://openalex.org/W2962804345","https://openalex.org/W2963318081","https://openalex.org/W3038022836","https://openalex.org/W3038028469","https://openalex.org/W3105122387","https://openalex.org/W4297687186","https://openalex.org/W4300427714","https://openalex.org/W4318619660","https://openalex.org/W6675634716","https://openalex.org/W6728757088","https://openalex.org/W6738383168","https://openalex.org/W6759226220","https://openalex.org/W6759238902"],"related_works":["https://openalex.org/W4298221930","https://openalex.org/W2777914285","https://openalex.org/W4287823391","https://openalex.org/W3013363440","https://openalex.org/W4312762663","https://openalex.org/W4317941881","https://openalex.org/W3035927627","https://openalex.org/W3128909129","https://openalex.org/W4308527955","https://openalex.org/W2042102171"],"abstract_inverted_index":{"Federated":[0],"learning":[1,14,30,68,83,156],"is":[2,45,69],"a":[3,94,137],"state-of-the-art":[4],"technology":[5],"used":[6,62],"in":[7,32,36,105],"the":[8,28,37,60,74,79,87,111,116,121,128,132,145,149,167,176,189],"fog":[9],"computing,":[10],"which":[11],"allows":[12],"distributed":[13,42],"to":[15,101,126],"train":[16],"cross-device":[17],"data":[18,44,54,61,77,104,124,158],"while":[19,187],"achieving":[20,188],"efficient":[21],"performance.":[22],"Many":[23],"current":[24],"works":[25],"have":[26],"optimized":[27],"federated":[29,82,106],"algorithm":[31],"homogeneous":[33],"networks.":[34],"However,":[35],"actual":[38],"application":[39],"scenario":[40],"of":[41,76,81,131,140,152,169],"learning,":[43],"independently":[46],"generated":[47],"by":[48,63],"each":[49,64],"device,":[50],"and":[51,71,73,84,147,182],"this":[52,90],"non-homologous":[53],"has":[55],"different":[56],"distribution":[57],"characteristics.":[58],"Therefore,":[59],"device":[65],"for":[66],"local":[67,133],"unbalanced":[70,160],"non-IID,":[72],"heterogeneity":[75],"affects":[78],"performance":[80],"slows":[85],"down":[86],"convergence.":[88],"In":[89],"paper,":[91],"we":[92],"present":[93,148],"dynamic":[95,170],"sample":[96,113],"selection":[97],"optimization":[98],"algorithm,":[99],"FedSS,":[100],"tackle":[102],"heterogeneous":[103,180],"learning.":[107],"FedSS":[108],"dynamically":[109],"selects":[110],"training":[112],"size":[114],"during":[115],"gradient":[117],"iteration":[118],"based":[119],"on":[120],"locally":[122],"available":[123],"size,":[125],"settle":[127],"expensive":[129],"evaluations":[130],"objective":[134],"function":[135],"with":[136,179],"massive":[138],"amount":[139],"dataset.":[141],"We":[142],"theoretically":[143],"analyze":[144],"convergence":[146,177],"complexity":[150],"estimates":[151],"our":[153],"framework":[154],"when":[155],"large":[157],"from":[159],"distribution.":[161],"Our":[162],"experimental":[163],"results":[164],"show":[165],"that":[166],"use":[168],"sampling":[171],"methods":[172],"can":[173],"effectively":[174],"improve":[175],"speed":[178],"data,":[181],"keep":[183],"computational":[184],"costs":[185],"low":[186],"desired":[190],"accuracy.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":9}],"updated_date":"2026-04-23T09:07:50.710637","created_date":"2025-10-10T00:00:00"}