{"id":"https://openalex.org/W3156953231","doi":"https://doi.org/10.1145/3434770.3459734","title":"Accelerated Training via Device Similarity in Federated Learning","display_name":"Accelerated Training via Device Similarity in Federated Learning","publication_year":2021,"publication_date":"2021-04-10","ids":{"openalex":"https://openalex.org/W3156953231","doi":"https://doi.org/10.1145/3434770.3459734","mag":"3156953231"},"language":"en","primary_location":{"id":"doi:10.1145/3434770.3459734","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3434770.3459734","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th International Workshop on Edge Systems, Analytics and Networking","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/A5103211112","display_name":"Yuanli Wang","orcid":"https://orcid.org/0000-0002-9685-1859"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuanli Wang","raw_affiliation_strings":["University of Minnesota Minneapolis, Minnnesota, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota Minneapolis, Minnnesota, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053038544","display_name":"Joel Wolfrath","orcid":"https://orcid.org/0000-0002-7251-3740"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joel Wolfrath","raw_affiliation_strings":["University of Minnesota Minneapolis, Minnnesota, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota Minneapolis, Minnnesota, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049892613","display_name":"Nikhil Sreekumar","orcid":"https://orcid.org/0000-0001-6248-212X"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nikhil Sreekumar","raw_affiliation_strings":["University of Minnesota Minneapolis, Minnnesota, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota Minneapolis, Minnnesota, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103542225","display_name":"Dhruv Kumar","orcid":null},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dhruv Kumar","raw_affiliation_strings":["University of Minnesota Minneapolis, Minnnesota, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota Minneapolis, Minnnesota, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006850370","display_name":"Abhishek Chandra","orcid":"https://orcid.org/0000-0001-9914-2604"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhishek Chandra","raw_affiliation_strings":["University of Minnesota Minneapolis, Minnnesota, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota Minneapolis, Minnnesota, USA","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I130238516"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"31","last_page":"36"},"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/T13553","display_name":"Age of Information Optimization","score":0.9843000173568726,"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"}},{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9830999970436096,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8531255722045898},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6449587345123291},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5437383651733398},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5363361239433289},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47704046964645386},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.45861104130744934},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4485871493816376},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.4244411289691925},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.401029497385025},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.3854404091835022}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8531255722045898},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6449587345123291},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5437383651733398},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5363361239433289},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47704046964645386},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.45861104130744934},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4485871493816376},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.4244411289691925},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.401029497385025},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3854404091835022},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"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/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3434770.3459734","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3434770.3459734","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th International Workshop on Edge Systems, Analytics and Networking","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":0.5199999809265137}],"awards":[{"id":"https://openalex.org/G3377276987","display_name":null,"funder_award_id":"CNS-1717834","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2130903752","https://openalex.org/W2149197198","https://openalex.org/W2541884796","https://openalex.org/W2604783387","https://openalex.org/W2793925626","https://openalex.org/W2807006176","https://openalex.org/W2913570153","https://openalex.org/W2949786670","https://openalex.org/W2963653544","https://openalex.org/W2963803379","https://openalex.org/W2982312326","https://openalex.org/W3034713518","https://openalex.org/W3037871107","https://openalex.org/W3037913917","https://openalex.org/W3131074139","https://openalex.org/W4233358539","https://openalex.org/W4245507943","https://openalex.org/W4251866715","https://openalex.org/W4294106961"],"related_works":["https://openalex.org/W4298130764","https://openalex.org/W2804364458","https://openalex.org/W2132641928","https://openalex.org/W4310225030","https://openalex.org/W2090259340","https://openalex.org/W1926736923","https://openalex.org/W2158836806","https://openalex.org/W2393816671","https://openalex.org/W2083665254","https://openalex.org/W4381571012"],"abstract_inverted_index":{"Federated":[0],"Learning":[1],"is":[2,51],"a":[3,10,33,47,119,216],"privacy-preserving,":[4],"machine":[5],"learning":[6,121],"technique":[7],"that":[8,54,129,210],"generates":[9],"globally":[11],"shared":[12],"model":[13,16,76,80,111,113,153,157],"with":[14,132,141,182],"in-situ":[15],"training":[17,42,65,222],"on":[18,108,124],"distributed":[19],"devices.":[20],"These":[21],"systems":[22],"are":[23,56],"often":[24],"comprised":[25],"of":[26,28,35,105,180,202],"millions":[27],"user":[29],"devices":[30,37,55,131,140,181],"and":[31,64,78,115,189,197],"only":[32],"subset":[34],"available":[36],"can":[38,149,214],"be":[39],"used":[40],"for":[41,74,88,167,177,200],"in":[43,59,118,164,170,221,228],"each":[44,147,201],"epoch.":[45],"Designing":[46],"device":[48,70,89,109],"selection":[49,71,90],"strategy":[50],"challenging,":[52],"given":[53],"highly":[57],"heterogeneous":[58],"both":[60],"their":[61],"system":[62,86],"resources":[63],"data.":[66],"This":[67,159],"heterogeneity":[68,87,107],"makes":[69],"very":[72],"crucial":[73],"timely":[75],"convergence":[77,154],"sufficient":[79],"accuracy.":[81,158,232],"Existing":[82],"approaches":[83,227],"have":[84,92],"addressed":[85],"but":[91],"largely":[93],"ignored":[94],"the":[95,103,139,142,152,171,211,230],"data":[96,106,134,184],"heterogeneity.":[97],"In":[98],"this":[99],"work,":[100],"we":[101,127],"analyze":[102],"impact":[104],"selection,":[110],"convergence,":[112],"accuracy,":[114],"fault":[116,168],"tolerance":[117,169],"federated":[120],"setting.":[122],"Based":[123],"our":[125],"analysis,":[126],"propose":[128,174],"clustering":[130,160],"similar":[133,183],"distributions":[135],"followed":[136],"by":[137],"selecting":[138],"best":[143],"processing":[144],"capacity":[145],"from":[146],"cluster":[148],"significantly":[150],"improve":[151],"without":[155],"compromising":[156],"also":[161,187],"guides":[162],"us":[163],"designing":[165],"policies":[166],"system.":[172],"We":[173,186],"three":[175],"methods":[176,213],"identifying":[178],"groups":[179],"distributions.":[185],"identify":[188],"discuss":[190],"rich":[191],"trade-offs":[192],"between":[193],"privacy,":[194],"bandwidth":[195],"consumption,":[196],"computation":[198],"overhead":[199],"these":[203],"proposed":[204,212],"methods.":[205],"Our":[206],"preliminary":[207],"experiments":[208],"show":[209],"provide":[215],"46%":[217],"-":[218],"58%":[219],"reduction":[220],"time":[223],"compared":[224],"to":[225],"existing":[226],"reaching":[229],"same":[231]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}