{"id":"https://openalex.org/W4393371662","doi":"https://doi.org/10.1109/access.2024.3383783","title":"Experimenting With Normalization Layers in Federated Learning on Non-IID Scenarios","display_name":"Experimenting With Normalization Layers in Federated Learning on Non-IID Scenarios","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4393371662","doi":"https://doi.org/10.1109/access.2024.3383783"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3383783","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3383783","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10486903.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10486903.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005431414","display_name":"Bruno Casella","orcid":"https://orcid.org/0000-0002-9513-6087"},"institutions":[{"id":"https://openalex.org/I55143463","display_name":"University of Turin","ror":"https://ror.org/048tbm396","country_code":"IT","type":"education","lineage":["https://openalex.org/I55143463"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Bruno Casella","raw_affiliation_strings":["Department of Computer Science, University of Turin, Turin, Italy","Computer Science Dpt. of the University of Turin, Corso Svizzera 185, Turin, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Turin, Turin, Italy","institution_ids":["https://openalex.org/I55143463"]},{"raw_affiliation_string":"Computer Science Dpt. of the University of Turin, Corso Svizzera 185, Turin, Italy","institution_ids":["https://openalex.org/I55143463"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101971371","display_name":"Roberto Esposito","orcid":"https://orcid.org/0000-0001-5366-292X"},"institutions":[{"id":"https://openalex.org/I55143463","display_name":"University of Turin","ror":"https://ror.org/048tbm396","country_code":"IT","type":"education","lineage":["https://openalex.org/I55143463"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Roberto Esposito","raw_affiliation_strings":["Department of Computer Science, University of Turin, Turin, Italy","Computer Science Dpt. of the University of Turin, Corso Svizzera 185, Turin, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Turin, Turin, Italy","institution_ids":["https://openalex.org/I55143463"]},{"raw_affiliation_string":"Computer Science Dpt. of the University of Turin, Corso Svizzera 185, Turin, Italy","institution_ids":["https://openalex.org/I55143463"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026203822","display_name":"Antonio Sciarappa","orcid":"https://orcid.org/0000-0002-3829-2526"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Antonio Sciarappa","raw_affiliation_strings":["Leonardo S.p.A., Rome, Italy"],"affiliations":[{"raw_affiliation_string":"Leonardo S.p.A., Rome, Italy","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029838099","display_name":"Carlo Cavazzoni","orcid":"https://orcid.org/0000-0002-9589-4785"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Carlo Cavazzoni","raw_affiliation_strings":["Leonardo S.p.A., Rome, Italy"],"affiliations":[{"raw_affiliation_string":"Leonardo S.p.A., Rome, Italy","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089092418","display_name":"Marco Aldinucci","orcid":"https://orcid.org/0000-0001-8788-0829"},"institutions":[{"id":"https://openalex.org/I55143463","display_name":"University of Turin","ror":"https://ror.org/048tbm396","country_code":"IT","type":"education","lineage":["https://openalex.org/I55143463"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Marco Aldinucci","raw_affiliation_strings":["Department of Computer Science, University of Turin, Turin, Italy","Computer Science Dpt. of the University of Turin, Corso Svizzera 185, Turin, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Turin, Turin, Italy","institution_ids":["https://openalex.org/I55143463"]},{"raw_affiliation_string":"Computer Science Dpt. of the University of Turin, Corso Svizzera 185, Turin, Italy","institution_ids":["https://openalex.org/I55143463"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5005431414"],"corresponding_institution_ids":["https://openalex.org/I55143463"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":6.04,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.96662287,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"12","issue":null,"first_page":"47961","last_page":"47971"},"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":0.9980000257492065,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9908999800682068,"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/normalization","display_name":"Normalization (sociology)","score":0.8076215982437134},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8006694912910461},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4410484731197357}],"concepts":[{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.8076215982437134},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8006694912910461},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4410484731197357},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3383783","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3383783","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10486903.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:105e11334dad42d690a6c3e9135dec79","is_oa":true,"landing_page_url":"https://doaj.org/article/105e11334dad42d690a6c3e9135dec79","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 47961-47971 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3383783","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3383783","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10486903.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2020622295","display_name":null,"funder_award_id":"Big Data","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G3650733657","display_name":null,"funder_award_id":"NextGenerationEU","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G4324742202","display_name":null,"funder_award_id":"826647","funder_id":"https://openalex.org/F4320332999","funder_display_name":"Horizon 2020 Framework Programme"},{"id":"https://openalex.org/G4339894583","display_name":null,"funder_award_id":"826647","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G4937468798","display_name":null,"funder_award_id":"H2020","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G8051717526","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G8949667757","display_name":null,"funder_award_id":"Specific Grant","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320332999","display_name":"Horizon 2020 Framework Programme","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4393371662.pdf","grobid_xml":"https://content.openalex.org/works/W4393371662.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2194775991","https://openalex.org/W2502312327","https://openalex.org/W2560647685","https://openalex.org/W2744999500","https://openalex.org/W2955213239","https://openalex.org/W2963889845","https://openalex.org/W2980910578","https://openalex.org/W2995022099","https://openalex.org/W3038022836","https://openalex.org/W3048417775","https://openalex.org/W3091267000","https://openalex.org/W3118608800","https://openalex.org/W3163157233","https://openalex.org/W3211008974","https://openalex.org/W4213165694","https://openalex.org/W4250482878","https://openalex.org/W4287332481","https://openalex.org/W4304099129","https://openalex.org/W4307352647","https://openalex.org/W4312896266","https://openalex.org/W4378976856","https://openalex.org/W4385245566","https://openalex.org/W4386453635","https://openalex.org/W4390706297","https://openalex.org/W6638667902","https://openalex.org/W6724804524","https://openalex.org/W6726983090","https://openalex.org/W6728757088","https://openalex.org/W6730251247","https://openalex.org/W6733590821","https://openalex.org/W6759238902","https://openalex.org/W6762718338","https://openalex.org/W6765541894","https://openalex.org/W6769871928","https://openalex.org/W6773817997","https://openalex.org/W6781318954","https://openalex.org/W6789305514","https://openalex.org/W6804126845","https://openalex.org/W6839251007","https://openalex.org/W6851527765"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2591697403","https://openalex.org/W2944728705","https://openalex.org/W2904022177","https://openalex.org/W2359348847","https://openalex.org/W3011538607","https://openalex.org/W4294432981","https://openalex.org/W4321441197"],"abstract_inverted_index":{"Training":[0],"Deep":[1],"Learning":[2,17],"(DL)":[3],"models":[4],"require":[5],"large,":[6],"high-quality":[7],"datasets,":[8],"often":[9],"assembled":[10],"with":[11,137],"data":[12,106],"from":[13,33],"different":[14,34,94],"institutions.":[15],"Federated":[16],"(FL)":[18],"has":[19],"been":[20],"emerging":[21],"as":[22],"a":[23,75,138],"method":[24],"for":[25,97,118,126],"privacy-preserving":[26],"pooling":[27],"of":[28,47,78,104,141],"datasets":[29,52],"employing":[30],"collaborative":[31],"training":[32,98],"institutions":[35],"by":[36,74],"iteratively":[37],"globally":[38],"aggregating":[39],"locally":[40],"trained":[41],"models.":[42],"One":[43],"critical":[44],"performance":[45,139],"challenge":[46],"FL":[48],"is":[49,121],"operating":[50],"on":[51],"not":[53,122],"independently":[54],"and":[55,84,108,130,160],"identically":[56],"distributed":[57],"(non-IID)":[58],"among":[59],"the":[60,123,148],"federation":[61],"participants.":[62],"Even":[63],"though":[64],"this":[65,89],"fragility":[66],"cannot":[67],"be":[68,72],"eliminated,":[69],"it":[70],"can":[71],"debunked":[73],"suitable":[76],"optimization":[77],"two":[79,102,109],"hyper-parameters:":[80],"layer":[81],"normalization":[82,95],"methods":[83],"collaboration":[85],"frequency":[86],"selection.":[87],"In":[88],"work,":[90],"we":[91],"benchmark":[92],"five":[93],"layers":[96],"Neural":[99],"Networks":[100],"(NNs),":[101],"families":[103],"non-IID":[105,151],"skew,":[107],"datasets.":[110],"Results":[111],"show":[112],"that":[113],"Batch":[114,135],"Normalization,":[115,136],"widely":[116],"employed":[117],"centralized":[119],"DL,":[120],"best":[124],"choice":[125],"FL,":[127],"whereas":[128],"Group":[129],"Layer":[131],"Normalization":[132],"consistently":[133],"outperform":[134],"gain":[140],"up":[142],"to":[143],"about":[144],"15":[145],"%":[146],"in":[147],"most":[149],"challenging":[150],"scenario.":[152],"Similarly,":[153],"frequent":[154],"model":[155],"aggregation":[156],"decreases":[157],"convergence":[158],"speed":[159],"mode":[161],"quality.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":4}],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}