{"id":"https://openalex.org/W4408324550","doi":"https://doi.org/10.1109/globecom52923.2024.10901033","title":"Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning","display_name":"Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning","publication_year":2024,"publication_date":"2024-12-08","ids":{"openalex":"https://openalex.org/W4408324550","doi":"https://doi.org/10.1109/globecom52923.2024.10901033"},"language":"en","primary_location":{"id":"doi:10.1109/globecom52923.2024.10901033","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom52923.2024.10901033","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2024 - 2024 IEEE Global Communications Conference","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/A5033267594","display_name":"Meiying Zhang","orcid":"https://orcid.org/0000-0002-1785-573X"},"institutions":[{"id":"https://openalex.org/I94658018","display_name":"University of Memphis","ror":"https://ror.org/01cq23130","country_code":"US","type":"education","lineage":["https://openalex.org/I94658018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Meiying Zhang","raw_affiliation_strings":["University of Memphis,Dept. of Computer Science,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Memphis,Dept. of Computer Science,USA","institution_ids":["https://openalex.org/I94658018"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011902043","display_name":"Huan Zhao","orcid":"https://orcid.org/0000-0002-3133-3137"},"institutions":[{"id":"https://openalex.org/I94658018","display_name":"University of Memphis","ror":"https://ror.org/01cq23130","country_code":"US","type":"education","lineage":["https://openalex.org/I94658018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huan Zhao","raw_affiliation_strings":["University of Memphis,Dept. of Computer Science,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Memphis,Dept. of Computer Science,USA","institution_ids":["https://openalex.org/I94658018"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077619022","display_name":"Sheldon C. Ebron","orcid":null},"institutions":[{"id":"https://openalex.org/I94658018","display_name":"University of Memphis","ror":"https://ror.org/01cq23130","country_code":"US","type":"education","lineage":["https://openalex.org/I94658018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sheldon Ebron","raw_affiliation_strings":["University of Memphis,Dept. of Computer Science,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Memphis,Dept. of Computer Science,USA","institution_ids":["https://openalex.org/I94658018"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100329754","display_name":"Kan Yang","orcid":"https://orcid.org/0000-0003-4234-9596"},"institutions":[{"id":"https://openalex.org/I94658018","display_name":"University of Memphis","ror":"https://ror.org/01cq23130","country_code":"US","type":"education","lineage":["https://openalex.org/I94658018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kan Yang","raw_affiliation_strings":["University of Memphis,Dept. of Computer Science,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Memphis,Dept. of Computer Science,USA","institution_ids":["https://openalex.org/I94658018"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I94658018"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1635","last_page":"1640"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9995999932289124,"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":0.9995999932289124,"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/T10927","display_name":"Access Control and Trust","score":0.9799000024795532,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9697999954223633,"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":0.757659912109375},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.46325430274009705},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3255762457847595},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.32274651527404785}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.757659912109375},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.46325430274009705},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3255762457847595},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.32274651527404785}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom52923.2024.10901033","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom52923.2024.10901033","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2024 - 2024 IEEE Global Communications Conference","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":30,"referenced_works":["https://openalex.org/W2013976210","https://openalex.org/W2118388899","https://openalex.org/W2345285933","https://openalex.org/W2487898712","https://openalex.org/W2559840118","https://openalex.org/W2948176562","https://openalex.org/W2970773487","https://openalex.org/W2972882814","https://openalex.org/W3001989995","https://openalex.org/W3006921589","https://openalex.org/W3008477738","https://openalex.org/W3015636663","https://openalex.org/W3108051446","https://openalex.org/W3138597937","https://openalex.org/W3157680283","https://openalex.org/W3170790803","https://openalex.org/W3198837878","https://openalex.org/W3201030831","https://openalex.org/W4250739957","https://openalex.org/W4285225730","https://openalex.org/W6728757088","https://openalex.org/W6743821447","https://openalex.org/W6748786018","https://openalex.org/W6752600739","https://openalex.org/W6756840679","https://openalex.org/W6760064560","https://openalex.org/W6762234667","https://openalex.org/W6779045794","https://openalex.org/W6858059469","https://openalex.org/W7056673059"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4298221930","https://openalex.org/W2390279801","https://openalex.org/W2777914285","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W3176937389","https://openalex.org/W4408069290"],"abstract_inverted_index":{"Federated":[0],"Learning":[1],"(FL)":[2],"is":[3,27,35,126,138],"widely":[4],"applied":[5],"in":[6,13,96,169],"communication":[7],"networks.":[8],"The":[9,54],"performance":[10],"of":[11,24,56],"clients":[12,38,58,116],"FL":[14],"can":[15,162],"vary":[16],"due":[17],"to":[18,48,67,104],"various":[19],"reasons.":[20],"Assessing":[21],"the":[22,60,106],"contributions":[23,95,113,168],"each":[25],"client":[26,30,94,167],"crucial":[28],"for":[29,92],"selection":[31],"and":[32,42,87,131,149,164],"compensation.":[33],"It":[34],"challenging":[36],"because":[37],"often":[39],"have":[40],"non-independent":[41],"identically":[43],"distributed":[44],"(non-iid)":[45],"data,":[46],"leading":[47],"potentially":[49],"noisy":[50],"or":[51,71,154],"divergent":[52],"updates.":[53],"risk":[55],"malicious":[57,122],"amplifies":[59],"challenge":[61],"especially":[62],"when":[63],"there\u2019s":[64],"no":[65,151],"access":[66],"clients\u2019":[68],"local":[69,146],"data":[70],"a":[72,81,100,133,170],"benchmark":[73],"root":[74],"dataset.":[75],"In":[76],"this":[77],"paper,":[78],"we":[79],"introduce":[80],"novel":[82],"method":[83],"called":[84,102],"Fair,":[85],"Robust,":[86],"Efficient":[88],"Client":[89],"Assessment":[90],"(FRECA)":[91],"quantifying":[93],"FL.":[97],"FRECA":[98,137,161],"employs":[99],"framework":[101],"FedTruth":[103],"estimate":[105],"global":[107],"model\u2019s":[108],"ground":[109],"truth":[110],"update,":[111],"balancing":[112],"from":[114,121],"all":[115],"while":[117],"filtering":[118],"out":[119],"impacts":[120],"ones.":[123],"This":[124],"approach":[125],"robust":[127,171],"against":[128],"Byzantine":[129],"attacks":[130],"incorporates":[132],"Byzantine-resilient":[134],"aggregation":[135],"algorithm.":[136],"also":[139],"efficient,":[140],"as":[141],"it":[142],"operates":[143],"solely":[144],"on":[145],"model":[147],"updates":[148],"requires":[150],"validation":[152],"operations":[153],"datasets.":[155],"Our":[156],"experimental":[157],"results":[158],"show":[159],"that":[160],"accurately":[163],"efficiently":[165],"quantify":[166],"manner.":[172]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}