{"id":"https://openalex.org/W7123610634","doi":"https://doi.org/10.1109/iscc65549.2025.11326092","title":"SyndFL: Addressing Class Imbalance to Enhance Fairness in Healthcare Image Processing Through Syndicated Federated Learning","display_name":"SyndFL: Addressing Class Imbalance to Enhance Fairness in Healthcare Image Processing Through Syndicated Federated Learning","publication_year":2025,"publication_date":"2025-07-02","ids":{"openalex":"https://openalex.org/W7123610634","doi":"https://doi.org/10.1109/iscc65549.2025.11326092"},"language":null,"primary_location":{"id":"doi:10.1109/iscc65549.2025.11326092","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscc65549.2025.11326092","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Symposium on Computers and Communications (ISCC)","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/A5019579175","display_name":"Ferdinand Kahenga","orcid":null},"institutions":[{"id":"https://openalex.org/I869660684","display_name":"University of the Western Cape","ror":"https://ror.org/00h2vm590","country_code":"ZA","type":"education","lineage":["https://openalex.org/I869660684"]}],"countries":["ZA"],"is_corresponding":true,"raw_author_name":"Ferdinand Kahenga","raw_affiliation_strings":["University of the Western Cape,Department of Computer Science,Cape Town,South Africa"],"affiliations":[{"raw_affiliation_string":"University of the Western Cape,Department of Computer Science,Cape Town,South Africa","institution_ids":["https://openalex.org/I869660684"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063116320","display_name":"Antoine Bagula","orcid":"https://orcid.org/0000-0003-0774-5251"},"institutions":[{"id":"https://openalex.org/I869660684","display_name":"University of the Western Cape","ror":"https://ror.org/00h2vm590","country_code":"ZA","type":"education","lineage":["https://openalex.org/I869660684"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Antoine Bagula","raw_affiliation_strings":["University of the Western Cape,Department of Computer Science,Cape Town,South Africa"],"affiliations":[{"raw_affiliation_string":"University of the Western Cape,Department of Computer Science,Cape Town,South Africa","institution_ids":["https://openalex.org/I869660684"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050881965","display_name":"Sajal K. Das","orcid":"https://orcid.org/0000-0002-9471-0868"},"institutions":[{"id":"https://openalex.org/I869660684","display_name":"University of the Western Cape","ror":"https://ror.org/00h2vm590","country_code":"ZA","type":"education","lineage":["https://openalex.org/I869660684"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Sajal K. Das","raw_affiliation_strings":["University of the Western Cape,Department of Computer Science,Cape Town,South Africa"],"affiliations":[{"raw_affiliation_string":"University of the Western Cape,Department of Computer Science,Cape Town,South Africa","institution_ids":["https://openalex.org/I869660684"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5019579175"],"corresponding_institution_ids":["https://openalex.org/I869660684"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.85939061,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.7631999850273132,"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":0.7631999850273132,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.03099999949336052,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.01940000057220459,"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/robustness","display_name":"Robustness (evolution)","score":0.6840000152587891},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.6686999797821045},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.4925999939441681},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4706999957561493},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.4392000138759613},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4027000069618225},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.37220001220703125},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.35359999537467957}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.722100019454956},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6840000152587891},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6686999797821045},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.4925999939441681},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4706999957561493},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.4392000138759613},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4027000069618225},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3847000002861023},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.37220001220703125},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3513999879360199},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3246000111103058},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.32170000672340393},{"id":"https://openalex.org/C2988170871","wikidata":"https://www.wikidata.org/wiki/Q11000047","display_name":"Healthcare system","level":3,"score":0.31310001015663147},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C3017977704","wikidata":"https://www.wikidata.org/wiki/Q18745135","display_name":"Health data","level":3,"score":0.29660001397132874},{"id":"https://openalex.org/C2777466982","wikidata":"https://www.wikidata.org/wiki/Q5227287","display_name":"Data extraction","level":3,"score":0.2962000072002411},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.28790000081062317},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.2624000012874603}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscc65549.2025.11326092","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscc65549.2025.11326092","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Symposium on Computers and Communications (ISCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8049520254135132,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2015452969","https://openalex.org/W2513863019","https://openalex.org/W3196594756","https://openalex.org/W3208293033","https://openalex.org/W4366399428","https://openalex.org/W4387171780","https://openalex.org/W4392158775","https://openalex.org/W4392939845","https://openalex.org/W4392979532","https://openalex.org/W4402128718","https://openalex.org/W4407692293"],"related_works":[],"abstract_inverted_index":{"Federated":[0,64],"Learning":[1,65],"(FL)":[2],"is":[3],"increasingly":[4],"used":[5],"in":[6,28,49,116,135,151],"healthcare":[7,122],"to":[8,43,103,140],"enable":[9],"collaborative":[10],"model":[11,117,147],"training":[12],"across":[13],"decentralized":[14],"medical":[15,29],"institutions":[16],"while":[17],"preserving":[18],"patient":[19],"privacy.":[20],"Despite":[21],"its":[22],"promise,":[23],"FL":[24,142],"faces":[25],"significant":[26],"challenges":[27],"image":[30,123],"processing,":[31],"such":[32],"as":[33],"class":[34],"imbalance":[35],"and":[36,46,86,149,154],"client":[37,70,76],"data":[38,96],"heterogeneity,":[39],"which":[40],"can":[41],"lead":[42],"biased":[44],"models":[45],"reduced":[47],"accuracy":[48],"detecting":[50,136],"rare":[51,106,137],"diseases.":[52],"To":[53],"address":[54],"these":[55],"issues,":[56],"we":[57],"propose":[58],"a":[59,68,129],"novel":[60],"approach,":[61],"called":[62],"Syndicated":[63],"(SyndFL),":[66],"comprising":[67],"multi-layer":[69],"selection":[71],"algorithm":[72],"that":[73,105,126],"emphasizes":[74],"fair":[75],"representation":[77],"based":[78],"on":[79,121],"dataset":[80],"size,":[81],"learning":[82],"performance,":[83],"label":[84],"distribution,":[85],"domain-specific":[87],"features.":[88],"SyndFL":[89,127],"not":[90],"only":[91],"prioritizes":[92],"clients":[93],"from":[94],"minority":[95],"clusters":[97],"but":[98],"also":[99],"includes":[100],"adaptive":[101],"weighting":[102],"ensure":[104],"conditions":[107,138],"receive":[108],"adequate":[109],"representation,":[110],"reducing":[111],"the":[112],"risk":[113],"of":[114],"bias":[115],"aggregation.":[118],"Experiments":[119],"conducted":[120],"datasets":[124],"demonstrate":[125],"achieves":[128],"$\\mathbf{1":[130],"0}-\\mathbf{2":[131],"0":[132],"\\%}$":[133],"improvement":[134],"compared":[139],"standard":[141],"methods,":[143],"significantly":[144],"enhancing":[145],"both":[146],"robustness":[148],"fairness":[150],"clinical":[152],"decision-making":[153],"diagnostics.":[155]},"counts_by_year":[],"updated_date":"2026-01-14T23:44:37.837170","created_date":"2026-01-14T00:00:00"}
