{"id":"https://openalex.org/W4406458097","doi":"https://doi.org/10.1109/bigdata62323.2024.10825520","title":"ELFS: Entropy-based Loss Function Selection for Global Model Accuracy in Federated Learning","display_name":"ELFS: Entropy-based Loss Function Selection for Global Model Accuracy in Federated Learning","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406458097","doi":"https://doi.org/10.1109/bigdata62323.2024.10825520"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825520","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825520","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5101573629","display_name":"Sunghwan Park","orcid":"https://orcid.org/0009-0007-5361-6684"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Sunghwan Park","raw_affiliation_strings":["Chung-Ang University,Business Administration,Seoul,Korea","Chung-Ang University,Security Convergence Science,Seoul,Korea"],"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Business Administration,Seoul,Korea","institution_ids":["https://openalex.org/I67900169"]},{"raw_affiliation_string":"Chung-Ang University,Security Convergence Science,Seoul,Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100681614","display_name":"Sangho Park","orcid":"https://orcid.org/0000-0002-6046-7425"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sangho Park","raw_affiliation_strings":["Chung-Ang University,Business Administration,Seoul,Korea","Chung-Ang University,Security Convergence Science,Seoul,Korea"],"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Business Administration,Seoul,Korea","institution_ids":["https://openalex.org/I67900169"]},{"raw_affiliation_string":"Chung-Ang University,Security Convergence Science,Seoul,Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066848156","display_name":"Seung\u2010Hoon Na","orcid":"https://orcid.org/0000-0002-4372-7125"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sunwoo Na","raw_affiliation_strings":["Chung-Ang University,Computer Science and Engineering,Seoul,Korea"],"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Computer Science and Engineering,Seoul,Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111222750","display_name":"Yihe Chang","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yeseul Chang","raw_affiliation_strings":["Chung-Ang University,Security Convergence Science,Seoul,Korea"],"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Security Convergence Science,Seoul,Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100415712","display_name":"Jaewoo Lee","orcid":"https://orcid.org/0000-0001-5887-2184"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaewoo Lee","raw_affiliation_strings":["Chung-Ang University,Industrial Security,Seoul,Korea"],"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Industrial Security,Seoul,Korea","institution_ids":["https://openalex.org/I67900169"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101573629"],"corresponding_institution_ids":["https://openalex.org/I67900169"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.23685385,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7991","last_page":"7997"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9779000282287598,"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":0.9696000218391418,"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":0.7674628496170044},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5246717929840088},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5039657950401306},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.49249085783958435},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48796290159225464},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46711409091949463},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.426529198884964}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7674628496170044},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5246717929840088},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5039657950401306},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.49249085783958435},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48796290159225464},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46711409091949463},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.426529198884964},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825520","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825520","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320311649","display_name":"Ministry of Education","ror":"https://ror.org/036nq5137"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2132083787","https://openalex.org/W2803187616","https://openalex.org/W2807006176","https://openalex.org/W2884561390","https://openalex.org/W2963819344","https://openalex.org/W2976335444","https://openalex.org/W2990789643","https://openalex.org/W2994684563","https://openalex.org/W3006017224","https://openalex.org/W3035453001","https://openalex.org/W3099314130","https://openalex.org/W3196371845","https://openalex.org/W4226101686","https://openalex.org/W4281563618","https://openalex.org/W4287332481","https://openalex.org/W4287906413","https://openalex.org/W4300427714","https://openalex.org/W4385236728","https://openalex.org/W6728757088","https://openalex.org/W6738383168","https://openalex.org/W6740984670","https://openalex.org/W6742348326","https://openalex.org/W6751420435","https://openalex.org/W6752029299","https://openalex.org/W6759238902","https://openalex.org/W6768570320","https://openalex.org/W6770590064","https://openalex.org/W6771652451","https://openalex.org/W6772307254","https://openalex.org/W6772318479","https://openalex.org/W6780224944","https://openalex.org/W6784336702","https://openalex.org/W6791444617","https://openalex.org/W6838630560","https://openalex.org/W6852177738"],"related_works":["https://openalex.org/W4298221930","https://openalex.org/W2777914285","https://openalex.org/W4378677776","https://openalex.org/W3013363440","https://openalex.org/W4287823391","https://openalex.org/W4312762663","https://openalex.org/W4317941881","https://openalex.org/W4229067761","https://openalex.org/W4308527955","https://openalex.org/W4366829214"],"abstract_inverted_index":{"Federated":[0],"Learning":[1],"(FL)":[2],"is":[3],"a":[4,18,31],"paradigm":[5],"that":[6,142],"enables":[7],"collaborative":[8],"training":[9,79],"while":[10],"keeping":[11],"data":[12,27,70,101,130],"localized,":[13],"avoiding":[14],"direct":[15],"sharing":[16],"with":[17],"central":[19],"server.":[20],"However,":[21],"Non-IID":[22,136,195],"(Non-Independent":[23],"and":[24,89,139,161],"Identically":[25],"Distributed)":[26],"across":[28],"clients":[29],"presents":[30],"significant":[32],"challenge":[33],"for":[34,182,189],"FL,":[35],"compromising":[36],"the":[37,60,65,82,100,112,127,170],"global":[38,54,145],"model":[39,55,146],"performance.":[40],"In":[41],"this":[42],"paper,":[43],"we":[44,167],"propose":[45],"Entropy-based":[46],"Loss":[47],"Function":[48],"Selection":[49],"(ELFS),":[50],"designed":[51],"to":[52,80,125,150,153,175],"enhance":[53],"accuracy":[56,147],"by":[57,148],"selectively":[58],"adapting":[59],"loss":[61,84,92,114,123,185],"function":[62,93,115,124],"based":[63,106],"on":[64,107,135],"entropy":[66,87,96,165],"of":[67,103,129,172],"each":[68,104,118],"client\u2019s":[69],"distribution.":[71],"ELFS":[72,143,179],"leverages":[73],"two":[74],"core":[75],"steps":[76],"before":[77],"local":[78],"determine":[81],"appropriate":[83,122],"function:":[85],"1)":[86],"calculation":[88,97],"2)":[90],"adaptive":[91,113],"selection.":[94],"The":[95],"step":[98],"quantifies":[99],"distribution":[102],"client":[105,119],"label":[108],"frequencies.":[109],"Subsequently,":[110],"in":[111,193],"selection":[116],"step,":[117],"selects":[120],"an":[121],"mitigate":[126],"impact":[128],"imbalance.":[131],"Our":[132],"experimental":[133],"results":[134],"datasets,":[137],"CIFAR-10":[138],"CIFAR-100,":[140],"demonstrate":[141,169],"improves":[144],"up":[149],"16.13%":[151],"compared":[152],"conventional":[154],"FL":[155],"methods,":[156],"such":[157],"as":[158],"FedAvg,":[159],"FedProx,":[160],"FedPer.":[162],"By":[163],"optimizing":[164],"thresholds,":[166],"further":[168,190],"importance":[171],"fine-tuning":[173],"hyperparameters":[174],"maximize":[176],"accuracy.":[177],"Moreover,":[178],"offers":[180],"flexibility":[181],"integrating":[183],"additional":[184],"functions,":[186],"providing":[187],"potential":[188],"performance":[191],"improvements":[192],"handling":[194],"data.":[196]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
