{"id":"https://openalex.org/W4389042807","doi":"https://doi.org/10.1109/isncc58260.2023.10323644","title":"Adaptive Ratio-Based-Threshold Gradient Sparsification Scheme for Federated Learning","display_name":"Adaptive Ratio-Based-Threshold Gradient Sparsification Scheme for Federated Learning","publication_year":2023,"publication_date":"2023-10-23","ids":{"openalex":"https://openalex.org/W4389042807","doi":"https://doi.org/10.1109/isncc58260.2023.10323644"},"language":"en","primary_location":{"id":"doi:10.1109/isncc58260.2023.10323644","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isncc58260.2023.10323644","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Symposium on Networks, Computers and Communications (ISNCC)","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/A5113332166","display_name":"Jeong Min Kong","orcid":null},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jeong Min Kong","raw_affiliation_strings":["University of Toronto,Department of Electrical and Computer Engineering,Toronto,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Toronto,Department of Electrical and Computer Engineering,Toronto,Canada","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109433489","display_name":"E.S. Sousa","orcid":null},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Elvino Sousa","raw_affiliation_strings":["University of Toronto,Department of Electrical and Computer Engineering,Toronto,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Toronto,Department of Electrical and Computer Engineering,Toronto,Canada","institution_ids":["https://openalex.org/I185261750"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3263,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66522833,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9976999759674072,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9866999983787537,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/mnist-database","display_name":"MNIST database","score":0.7617624998092651},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.726829469203949},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6024186611175537},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5651946663856506},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5423761606216431},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5264590382575989},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.45982953906059265},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.45325973629951477},{"id":"https://openalex.org/keywords/constant","display_name":"Constant (computer programming)","score":0.4378582537174225},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.43568509817123413},{"id":"https://openalex.org/keywords/independent-and-identically-distributed-random-variables","display_name":"Independent and identically distributed random variables","score":0.416492760181427},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3613874316215515},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3464270830154419},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3148638606071472},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.2886486053466797},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.21908089518547058},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.19850319623947144},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17109283804893494}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.7617624998092651},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.726829469203949},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6024186611175537},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5651946663856506},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5423761606216431},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5264590382575989},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.45982953906059265},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.45325973629951477},{"id":"https://openalex.org/C2777027219","wikidata":"https://www.wikidata.org/wiki/Q1284190","display_name":"Constant (computer programming)","level":2,"score":0.4378582537174225},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.43568509817123413},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.416492760181427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3613874316215515},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3464270830154419},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3148638606071472},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2886486053466797},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.21908089518547058},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.19850319623947144},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17109283804893494},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isncc58260.2023.10323644","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isncc58260.2023.10323644","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Symposium on Networks, Computers and Communications (ISNCC)","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":15,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2405578611","https://openalex.org/W2606891064","https://openalex.org/W3048121440","https://openalex.org/W3101036738","https://openalex.org/W3107557773","https://openalex.org/W3118608800","https://openalex.org/W3129329365","https://openalex.org/W3133504636","https://openalex.org/W3173523152","https://openalex.org/W4230874317","https://openalex.org/W4382237515","https://openalex.org/W6728757088","https://openalex.org/W6754341472","https://openalex.org/W6781891088"],"related_works":["https://openalex.org/W2950475743","https://openalex.org/W4386603768","https://openalex.org/W2886711096","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W2590796488","https://openalex.org/W4389249638","https://openalex.org/W2733410219","https://openalex.org/W2734358244","https://openalex.org/W2735450835"],"abstract_inverted_index":{"Federated":[0],"learning":[1,6],"(FL)":[2],"is":[3,106,171],"a":[4,73,153,173,188],"distributed":[5],"paradigm":[7],"that":[8,55,105,129,156,211,232],"has":[9],"received":[10],"great":[11],"attention":[12],"over":[13],"the":[14,24,45,52,67,80,93,98,116,119,122,136,144,215,223],"past":[15],"several":[16],"years":[17],"due":[18],"to":[19,43,66,85,140,235],"its":[20,109],"privacy-preserving":[21],"property.":[22],"As":[23],"models":[25],"involved":[26],"in":[27,40,143,204],"FL":[28],"are":[29,37,56,130,233],"usually":[30],"dense":[31],"and":[32,121,187,201,208],"overparameterized":[33],"however,":[34],"various":[35],"studies":[36],"being":[38],"conducted":[39],"gradient":[41,100,120,165,175,216],"sparsification":[42,101,166],"reduce":[44],"high":[46],"communication":[47,81],"overhead.":[48],"While":[49],"many":[50],"of":[51,58,79,90,132,159],"recently-presented":[53],"schemes":[54],"variations":[57],"top-k":[59],"have":[60,72],"shown":[61],"competitive":[62,234],"inference":[63,230],"accuracy":[64],"convergence":[65],"baseline":[68],"\u201cvanilla\u201d":[69],"FL,":[70],"they":[71],"fixed":[74],"sparsity":[75,176,217],"rate":[76],"throughout":[77],"all":[78],"rounds,":[82],"which":[83],"leads":[84],"an":[86],"unnecessary":[87],"excessive":[88],"transmission":[89],"gradients":[91,128],"as":[92,191,222],"global":[94,224],"model":[95,225],"converges.":[96],"Furthermore,":[97],"constant-threshold":[99],"method":[102],"called":[103,162],"Threshold-z-,":[104],"well-known":[107],"for":[108,115,178],"dynamic":[110],"rate,":[111],"does":[112],"not":[113],"account":[114],"ratio":[117],"between":[118],"pre-update":[123,137,185],"parameter":[124,138,181],"value,":[125],"causing":[126],"some":[127],"orders":[131],"magnitude":[133],"larger":[134],"than":[135],"values":[139],"be":[141],"neglected":[142],"following":[145],"aggregation":[146],"process.":[147],"In":[148],"this":[149],"paper,":[150],"we":[151],"introduce":[152],"new":[154],"algorithm":[155],"addresses":[157],"both":[158,205],"these":[160],"issues,":[161],"adaptive":[163],"ratio-based-threshold":[164],"method.":[167],"Our":[168],"main":[169],"idea":[170],"introducing":[172],"custom":[174],"threshold":[177],"each":[179],"local":[180],"based":[182],"on":[183,199],"their":[184],"value":[186],"hyperparameter":[189],"denoted":[190],"\u03a8.":[192],"We":[193],"demonstrate":[194],"through":[195],"image":[196],"classification":[197],"experiments":[198],"MNIST":[200],"CIFAR-10":[202],"datasets":[203],"independent-and-identically-distributed":[206],"(IID)":[207],"non-IID":[209],"settings":[210],"under":[212],"optimal":[213],"\u03a8,":[214],"rates":[218],"adapt":[219],"&":[220],"increase":[221],"converges,":[226],"while":[227],"simultaneously":[228],"producing":[229],"accuracies":[231],"vanilla":[236],"FL.":[237]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}