{"id":"https://openalex.org/W4318147391","doi":"https://doi.org/10.1109/bigdata55660.2022.10020923","title":"FedProb: An Aggregation Method Based on Feature Probability Distribution for Federated Learning on Non-IID Data","display_name":"FedProb: An Aggregation Method Based on Feature Probability Distribution for Federated Learning on Non-IID Data","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318147391","doi":"https://doi.org/10.1109/bigdata55660.2022.10020923"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020923","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020923","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","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/A5068818841","display_name":"Do-Van Nguyen","orcid":null},"institutions":[{"id":"https://openalex.org/I90023481","display_name":"National Institute of Information and Communications Technology","ror":"https://ror.org/016bgq349","country_code":"JP","type":"facility","lineage":["https://openalex.org/I90023481"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Do-Van Nguyen","raw_affiliation_strings":["National Institute of Information and Communications Technology (NICT),Big Data Integration Research Center,Tokyo,Japan","Big Data Integration Research Center, National Institute of Information and Communications Technology (NICT), Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Information and Communications Technology (NICT),Big Data Integration Research Center,Tokyo,Japan","institution_ids":["https://openalex.org/I90023481"]},{"raw_affiliation_string":"Big Data Integration Research Center, National Institute of Information and Communications Technology (NICT), Tokyo, Japan","institution_ids":["https://openalex.org/I90023481"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Anh-Khoa Tran","orcid":null},"institutions":[{"id":"https://openalex.org/I90023481","display_name":"National Institute of Information and Communications Technology","ror":"https://ror.org/016bgq349","country_code":"JP","type":"facility","lineage":["https://openalex.org/I90023481"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Anh-Khoa Tran","raw_affiliation_strings":["National Institute of Information and Communications Technology (NICT),Big Data Integration Research Center,Tokyo,Japan","Big Data Integration Research Center, National Institute of Information and Communications Technology (NICT), Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Information and Communications Technology (NICT),Big Data Integration Research Center,Tokyo,Japan","institution_ids":["https://openalex.org/I90023481"]},{"raw_affiliation_string":"Big Data Integration Research Center, National Institute of Information and Communications Technology (NICT), Tokyo, Japan","institution_ids":["https://openalex.org/I90023481"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048072689","display_name":"Koji Zettsu","orcid":"https://orcid.org/0000-0003-4062-2376"},"institutions":[{"id":"https://openalex.org/I90023481","display_name":"National Institute of Information and Communications Technology","ror":"https://ror.org/016bgq349","country_code":"JP","type":"facility","lineage":["https://openalex.org/I90023481"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Koji Zettsu","raw_affiliation_strings":["National Institute of Information and Communications Technology (NICT),Big Data Integration Research Center,Tokyo,Japan","Big Data Integration Research Center, National Institute of Information and Communications Technology (NICT), Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Information and Communications Technology (NICT),Big Data Integration Research Center,Tokyo,Japan","institution_ids":["https://openalex.org/I90023481"]},{"raw_affiliation_string":"Big Data Integration Research Center, National Institute of Information and Communications Technology (NICT), Tokyo, Japan","institution_ids":["https://openalex.org/I90023481"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5068818841"],"corresponding_institution_ids":["https://openalex.org/I90023481"],"apc_list":null,"apc_paid":null,"fwci":0.626,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.69035506,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2875","last_page":"2881"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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":1.0,"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/T10237","display_name":"Cryptography and Data Security","score":0.9861999750137329,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9710000157356262,"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":0.7339093089103699},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6772624850273132},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.5444378852844238},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45976513624191284},{"id":"https://openalex.org/keywords/data-aggregator","display_name":"Data aggregator","score":0.4528449773788452},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.44685229659080505},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44242218136787415},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32751011848449707},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23605072498321533},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1277426779270172},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.05774673819541931}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7339093089103699},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6772624850273132},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.5444378852844238},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45976513624191284},{"id":"https://openalex.org/C82578977","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Data aggregator","level":3,"score":0.4528449773788452},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.44685229659080505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44242218136787415},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32751011848449707},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23605072498321533},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1277426779270172},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.05774673819541931},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020923","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020923","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","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":21,"referenced_works":["https://openalex.org/W2001610032","https://openalex.org/W2116612304","https://openalex.org/W2162289206","https://openalex.org/W2194775991","https://openalex.org/W2995022099","https://openalex.org/W3033988836","https://openalex.org/W3081880709","https://openalex.org/W3090779976","https://openalex.org/W3103852474","https://openalex.org/W3118608800","https://openalex.org/W3196371845","https://openalex.org/W3199488740","https://openalex.org/W4205155256","https://openalex.org/W4206352836","https://openalex.org/W4287332481","https://openalex.org/W4300928835","https://openalex.org/W6637373629","https://openalex.org/W6728757088","https://openalex.org/W6759238902","https://openalex.org/W6781318954","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2793666424","https://openalex.org/W1521063997","https://openalex.org/W2558765606","https://openalex.org/W2357958079","https://openalex.org/W3159884765","https://openalex.org/W2114569715","https://openalex.org/W92316441","https://openalex.org/W3143258048","https://openalex.org/W2184725937","https://openalex.org/W2158851211"],"abstract_inverted_index":{"Federated":[0],"learning":[1,21],"(FL)":[2],"has":[3],"been":[4],"used":[5],"to":[6,23,36,88,97,103],"protect":[7],"data":[8,56],"contributors\u2019":[9],"privacy":[10],"by":[11],"allowing":[12],"training":[13],"at":[14],"clients":[15],"and":[16,58,65,137],"then":[17],"feeding":[18],"back":[19],"machine":[20],"models":[22,77],"servers":[24],"for":[25,119],"aggregation.":[26],"Conventional":[27],"methods":[28,32,95,140],"of":[29,54,101],"FL":[30,60,93,143],"aggregation":[31,94,125,129],"average":[33],"model":[34,82],"weights":[35],"produce":[37],"a":[38,117],"fused":[39],"global":[40,76],"model.":[41],"However,":[42],"in":[43,46,124,141],"real-world":[44],"applications":[45],"cyber-space":[47],"systems,":[48],"which":[49],"often":[50],"have":[51],"heterogeneous":[52],"Internet":[53],"Things":[55],"configuration":[57],"collection,":[59],"encounters":[61],"obstacles":[62],"with":[63],"non-independent":[64],"identically":[66],"distributed":[67],"(Non-IID)":[68],"data.":[69],"The":[70,127],"main":[71],"problem":[72],"is":[73],"the":[74,80,99,104],"aggregated":[75],"deviating":[78],"from":[79,109],"optimal":[81,105],"trained":[83,110],"on":[84,133],"centralized":[85],"servers.":[86],"According":[87],"recent":[89],"research,":[90],"most":[91],"Non-IID":[92,135],"attempt":[96],"direct":[98],"movement":[100],"gradients":[102],"one":[106],"using":[107,120],"differentiation":[108],"models.":[111],"In":[112],"this":[113],"paper,":[114],"we":[115],"propose":[116],"framework":[118],"feature":[121],"probability":[122],"distribution":[123],"calculation.":[126],"proposed":[128],"algorithm":[130],"shows":[131],"robustness":[132],"different":[134],"datasets":[136],"outperforms":[138],"state-of-the-art":[139],"various":[142],"experiments.":[144]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
