{"id":"https://openalex.org/W4386432042","doi":"https://doi.org/10.1109/jiot.2023.3311690","title":"Incentive Design for Heterogeneous Client Selection: A Robust Federated Learning Approach","display_name":"Incentive Design for Heterogeneous Client Selection: A Robust Federated Learning Approach","publication_year":2023,"publication_date":"2023-09-04","ids":{"openalex":"https://openalex.org/W4386432042","doi":"https://doi.org/10.1109/jiot.2023.3311690"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2023.3311690","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2023.3311690","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-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/A5092741099","display_name":"Papa Pene","orcid":"https://orcid.org/0009-0006-7892-2162"},"institutions":[{"id":"https://openalex.org/I4322298","display_name":"Towson University","ror":"https://ror.org/044w7a341","country_code":"US","type":"education","lineage":["https://openalex.org/I4322298"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Papa Pene","raw_affiliation_strings":["Department of Computer and Information Sciences, Towson University, Towson, MD, USA"],"raw_orcid":"https://orcid.org/0009-0006-7892-2162","affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences, Towson University, Towson, MD, USA","institution_ids":["https://openalex.org/I4322298"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083406034","display_name":"Weixian Liao","orcid":"https://orcid.org/0000-0003-1444-8925"},"institutions":[{"id":"https://openalex.org/I4322298","display_name":"Towson University","ror":"https://ror.org/044w7a341","country_code":"US","type":"education","lineage":["https://openalex.org/I4322298"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weixian Liao","raw_affiliation_strings":["Department of Computer and Information Sciences, Towson University, Towson, MD, USA"],"raw_orcid":"https://orcid.org/0000-0003-1444-8925","affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences, Towson University, Towson, MD, USA","institution_ids":["https://openalex.org/I4322298"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002139930","display_name":"Wei Yu","orcid":"https://orcid.org/0000-0003-4522-7340"},"institutions":[{"id":"https://openalex.org/I4322298","display_name":"Towson University","ror":"https://ror.org/044w7a341","country_code":"US","type":"education","lineage":["https://openalex.org/I4322298"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Yu","raw_affiliation_strings":["Department of Computer and Information Sciences, Towson University, Towson, MD, USA"],"raw_orcid":"https://orcid.org/0000-0003-4522-7340","affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences, Towson University, Towson, MD, USA","institution_ids":["https://openalex.org/I4322298"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4322298"],"apc_list":null,"apc_paid":null,"fwci":3.6994,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.94515154,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"11","issue":"4","first_page":"5939","last_page":"5950"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9945999979972839,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9944999814033508,"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":0.8169199824333191},{"id":"https://openalex.org/keywords/incentive","display_name":"Incentive","score":0.7804200649261475},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5827378034591675},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.3911445140838623},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.3894549012184143},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23296868801116943},{"id":"https://openalex.org/keywords/microeconomics","display_name":"Microeconomics","score":0.11122611165046692}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8169199824333191},{"id":"https://openalex.org/C29122968","wikidata":"https://www.wikidata.org/wiki/Q1414816","display_name":"Incentive","level":2,"score":0.7804200649261475},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5827378034591675},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3911445140838623},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3894549012184143},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23296868801116943},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.11122611165046692},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2023.3311690","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2023.3311690","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[{"id":"https://openalex.org/G8132319448","display_name":null,"funder_award_id":"FA9550-20-1-0418","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"}],"funders":[{"id":"https://openalex.org/F4320338279","display_name":"Air Force Office of Scientific Research","ror":"https://ror.org/011e9bt93"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1486587973","https://openalex.org/W2085648025","https://openalex.org/W2165232124","https://openalex.org/W2168231600","https://openalex.org/W2800017313","https://openalex.org/W2807006176","https://openalex.org/W2886444620","https://openalex.org/W2970408908","https://openalex.org/W2982255332","https://openalex.org/W2989289980","https://openalex.org/W2990595670","https://openalex.org/W2995022099","https://openalex.org/W2998696623","https://openalex.org/W3006921589","https://openalex.org/W3019945581","https://openalex.org/W3022321359","https://openalex.org/W3042029390","https://openalex.org/W3118608800","https://openalex.org/W3123411108","https://openalex.org/W3173670432","https://openalex.org/W3198837878","https://openalex.org/W3208283650","https://openalex.org/W4213201928","https://openalex.org/W4285242894","https://openalex.org/W4289147229","https://openalex.org/W4298221930","https://openalex.org/W4312231739","https://openalex.org/W6684859321","https://openalex.org/W6728757088","https://openalex.org/W6743821447","https://openalex.org/W6746720608","https://openalex.org/W6748786018","https://openalex.org/W6748805329","https://openalex.org/W6752029299","https://openalex.org/W6754708698","https://openalex.org/W6756756286","https://openalex.org/W6757643485","https://openalex.org/W6759238902","https://openalex.org/W6771533808","https://openalex.org/W6780188572","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2367468089","https://openalex.org/W2390279801","https://openalex.org/W2358591856","https://openalex.org/W4391913857","https://openalex.org/W2098133976","https://openalex.org/W2358668433","https://openalex.org/W2347416728"],"abstract_inverted_index":{"Federated":[0],"learning":[1,9],"(FL)":[2],"allows":[3],"the":[4,25,40,45,56,69,76,91,125,129,159,170,192,203,212,222,238,242],"collaborative":[5],"training":[6],"of":[7,39,68,79,90,98,150,161,172,205,225,247],"machine":[8],"(ML)":[10],"models":[11],"between":[12],"an":[13,115],"aggregation":[14],"server":[15,46,57,70],"and":[16,86,101,147,169,228],"different":[17],"clients":[18,37,48,151,177],"without":[19],"sharing":[20],"their":[21,62,154],"private":[22],"data.":[23],"However,":[24],"FL":[26,105],"archetype":[27],"is":[28,198],"mostly":[29],"vulnerable":[30],"to":[31,123,157,164,188,201],"malicious":[32],"model":[33,92],"updates":[34],"from":[35,176],"various":[36],"because":[38],"privacy":[41],"feature":[42],"that":[43,219],"makes":[44],"see":[47],"as":[49],"a":[50,83,88,138,185,214],"black":[51],"box.":[52],"When":[53],"selecting":[54],"clients,":[55],"has":[58,107],"no":[59],"control":[60],"on":[61,104,153,245],"contributions":[63],"during":[64],"training.":[65],"This":[66,195],"opacity":[67],"toward":[71],"clients\u2019":[72,165],"data":[73,81,102,174,226],"associated":[74],"with":[75,235],"huge":[77],"amount":[78],"heterogeneous":[80,119],"brings":[82],"security":[84,130],"risk":[85],"poses":[87],"deterioration":[89],"performance":[93,126],"in":[94,132,178],"FL.":[95,133,179,248],"The":[96,134],"impact":[97],"client":[99,120,140,190,230],"selection":[100,121,141],"heterogeneity":[103,155,227],"robustness":[106,246],"been":[108],"overlooked.":[109],"In":[110],"this":[111],"article,":[112],"we":[113,183],"develop":[114],"incentive":[116],"design":[117],"for":[118],"(IHCS)":[122],"improve":[124],"while":[127],"reducing":[128],"risks":[131],"IHCS":[135],"approach":[136,240],"applies":[137],"smarter":[139],"method":[142,218],"using":[143,191],"cooperative":[144],"game":[145],"theory":[146],"dynamic":[148],"clustering":[149,216],"based":[152],"level":[156],"overcome":[158],"challenges":[160],"lacking":[162],"access":[163],"data,":[166,168],"unbalanced":[167],"lack":[171],"applicable":[173],"contribution":[175],"To":[180],"do":[181],"so,":[182],"attribute":[184],"recognition":[186,196],"value":[187],"each":[189],"Shapley":[193],"value.":[194],"index":[197],"then":[199],"used":[200],"aggregate":[202],"probability":[204],"participation":[206],"level.":[207],"We":[208],"also":[209],"implement,":[210],"within":[211],"IHCS,":[213],"heterogeneity-based":[215],"(HIC)":[217],"helps":[220],"inhibit":[221],"negative":[223],"influence":[224],"increase":[229],"contributions.":[231],"Through":[232],"extensive":[233],"experiments":[234],"empirical":[236],"results,":[237],"proposed":[239],"outperforms":[241],"representative":[243],"works":[244]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":7}],"updated_date":"2026-07-18T07:39:51.176621","created_date":"2025-10-10T00:00:00"}
