{"id":"https://openalex.org/W3009065449","doi":"https://doi.org/10.1109/globecom38437.2019.9013625","title":"Robust Coreset Construction for Distributed Machine Learning","display_name":"Robust Coreset Construction for Distributed Machine Learning","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3009065449","doi":"https://doi.org/10.1109/globecom38437.2019.9013625","mag":"3009065449"},"language":"en","primary_location":{"id":"doi:10.1109/globecom38437.2019.9013625","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom38437.2019.9013625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Global Communications Conference (GLOBECOM)","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/A5004170004","display_name":"Hanlin Lu","orcid":"https://orcid.org/0000-0001-6743-301X"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hanlin Lu","raw_affiliation_strings":["Pennsylvania State University, University Park, PA, USA"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012864600","display_name":"Ming-Ju Li","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ming-Ju Li","raw_affiliation_strings":["Pennsylvania State University, University Park, PA, USA"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088400668","display_name":"Ting He","orcid":"https://orcid.org/0000-0003-1070-7483"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ting He","raw_affiliation_strings":["Pennsylvania State University, University Park, PA, USA"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100443968","display_name":"Shiqiang Wang","orcid":"https://orcid.org/0000-0003-2090-5512"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shiqiang Wang","raw_affiliation_strings":["IBM T. J. Watson Research Center, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center, NY, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101919131","display_name":"Vijaykrishnan Narayanan","orcid":"https://orcid.org/0000-0001-6266-6068"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vijaykrishnan Narayanan","raw_affiliation_strings":["Pennsylvania State University, University Park, PA, USA"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057402438","display_name":"Kevin Chan","orcid":"https://orcid.org/0000-0002-6425-5403"},"institutions":[{"id":"https://openalex.org/I166416128","display_name":"DEVCOM Army Research Laboratory","ror":"https://ror.org/011hc8f90","country_code":"US","type":"government","lineage":["https://openalex.org/I1304082316","https://openalex.org/I1330347796","https://openalex.org/I166416128","https://openalex.org/I2802705668","https://openalex.org/I4210154437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kevin S. Chan","raw_affiliation_strings":["Army Research Laboratory, Adelphi, MD, USA"],"affiliations":[{"raw_affiliation_string":"Army Research Laboratory, Adelphi, MD, USA","institution_ids":["https://openalex.org/I166416128"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5004170004"],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":null,"apc_paid":null,"fwci":0.9801,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.82919294,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"abs 1601 617","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":0.9972000122070312,"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":0.9972000122070312,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.994700014591217,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9927999973297119,"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":0.8049200773239136},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7466608285903931},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6690065264701843},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6007070541381836},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.47137200832366943},{"id":"https://openalex.org/keywords/online-machine-learning","display_name":"Online machine learning","score":0.4138694703578949},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.33166828751564026}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8049200773239136},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7466608285903931},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6690065264701843},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6007070541381836},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.47137200832366943},{"id":"https://openalex.org/C115903097","wikidata":"https://www.wikidata.org/wiki/Q7094097","display_name":"Online machine learning","level":3,"score":0.4138694703578949},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.33166828751564026},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom38437.2019.9013625","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom38437.2019.9013625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Global Communications Conference (GLOBECOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W150939725","https://openalex.org/W202059055","https://openalex.org/W1977263019","https://openalex.org/W1981773323","https://openalex.org/W1984675750","https://openalex.org/W2009332171","https://openalex.org/W2059651397","https://openalex.org/W2061902728","https://openalex.org/W2073459066","https://openalex.org/W2086959852","https://openalex.org/W2133962824","https://openalex.org/W2144335278","https://openalex.org/W2146200992","https://openalex.org/W2158275940","https://openalex.org/W2230030897","https://openalex.org/W2410099853","https://openalex.org/W2472333518","https://openalex.org/W2541884796","https://openalex.org/W2776050171","https://openalex.org/W2793925626","https://openalex.org/W2962788286","https://openalex.org/W2963879412","https://openalex.org/W2964185995","https://openalex.org/W3009065449","https://openalex.org/W4253418273","https://openalex.org/W4318619660","https://openalex.org/W6608211845","https://openalex.org/W6668990524","https://openalex.org/W6681503828","https://openalex.org/W6681580871","https://openalex.org/W6684187592","https://openalex.org/W6689526104","https://openalex.org/W6728757088"],"related_works":["https://openalex.org/W3206920865","https://openalex.org/W4286799911","https://openalex.org/W4206195464","https://openalex.org/W2990460313","https://openalex.org/W2339204188","https://openalex.org/W3101157325","https://openalex.org/W3033485676","https://openalex.org/W3159631642","https://openalex.org/W3210156800","https://openalex.org/W2969890106"],"abstract_inverted_index":{"Motivated":[0],"by":[1,105],"the":[2,14,20,28,32,40,51,62,95,145,149],"need":[3],"of":[4,16,27,34,61,91,97,125,148],"solving":[5],"machine":[6,76,83,126,140],"learning":[7,77,84,127,141],"problems":[8,128],"over":[9],"distributed":[10],"datasets,":[11],"we":[12,143],"explore":[13],"use":[15],"\\emph{coreset}":[17],"to":[18,45,80,88],"reduce":[19],"communication":[21,99],"overhead.":[22,100],"Coreset":[23],"is":[24],"a":[25,35,59,74,117,122],"summary":[26],"original":[29,63],"dataset":[30],"in":[31,39],"form":[33],"small":[36],"weighted":[37],"set":[38],"same":[41],"sample":[42],"space.":[43],"Compared":[44],"other":[46],"data":[47],"summaries,":[48],"coreset":[49,67,108],"has":[50,87],"advantage":[52],"that":[53,115],"it":[54],"can":[55],"be":[56],"used":[57],"as":[58],"proxy":[60],"dataset.":[64],"However,":[65],"existing":[66],"construction":[68,109],"algorithms":[69,110],"are":[70],"each":[71],"tailor-made":[72],"for":[73,121],"specific":[75],"problem.":[78],"Thus,":[79],"solve":[81],"different":[82,92],"problems,":[85,142],"one":[86],"collect":[89],"coresets":[90],"types,":[93],"defeating":[94],"purpose":[96],"saving":[98],"We":[101],"resolve":[102],"this":[103],"dilemma":[104],"developing":[106],"robust":[107,146],"based":[111],"on":[112,136],"k-means/median":[113],"clustering,":[114],"give":[116],"provably":[118],"good":[119],"approximation":[120],"broad":[123],"range":[124],"with":[129],"sufficiently":[130],"continuous":[131],"cost":[132],"functions.":[133],"Through":[134],"evaluations":[135],"diverse":[137],"datasets":[138],"and":[139],"verify":[144],"performance":[147],"proposed":[150],"algorithms.":[151]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}