{"id":"https://openalex.org/W7126060498","doi":"https://doi.org/10.1109/bibm66473.2025.11357066","title":"FedOCP-LRAT: A Rehearsal-Free Federated Continual Learning Framework via Orthogonal Compensation and Low-Rank Alternating Training for Medical Image Continual Classification","display_name":"FedOCP-LRAT: A Rehearsal-Free Federated Continual Learning Framework via Orthogonal Compensation and Low-Rank Alternating Training for Medical Image Continual Classification","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126060498","doi":"https://doi.org/10.1109/bibm66473.2025.11357066"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11357066","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11357066","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5124084365","display_name":"Hualong Cui","orcid":null},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hualong Cui","raw_affiliation_strings":["Inner Mongolia University,Hohhot,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inner Mongolia University,Hohhot,China","institution_ids":["https://openalex.org/I2722730"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124181499","display_name":"Yongqiang Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongqiang Gao","raw_affiliation_strings":["Inner Mongolia University,Hohhot,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inner Mongolia University,Hohhot,China","institution_ids":["https://openalex.org/I2722730"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124131133","display_name":"Yongmei Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongmei Liu","raw_affiliation_strings":["Inner Mongolia University,Hohhot,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inner Mongolia University,Hohhot,China","institution_ids":["https://openalex.org/I2722730"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124092987","display_name":"Mingyu Pang","orcid":null},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingyu Pang","raw_affiliation_strings":["Inner Mongolia University,Hohhot,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inner Mongolia University,Hohhot,China","institution_ids":["https://openalex.org/I2722730"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2722730"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3519","last_page":"3522"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6444000005722046,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6444000005722046,"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/T11448","display_name":"Face recognition and analysis","score":0.09099999815225601,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.07850000262260437,"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/overhead","display_name":"Overhead (engineering)","score":0.6463000178337097},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.5631999969482422},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.47870001196861267},{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.44589999318122864},{"id":"https://openalex.org/keywords/compensation","display_name":"Compensation (psychology)","score":0.42890000343322754},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.41670000553131104},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.39980000257492065},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.3995000123977661}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7795000076293945},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6463000178337097},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.5631999969482422},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5523999929428101},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.47870001196861267},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.44589999318122864},{"id":"https://openalex.org/C2780023022","wikidata":"https://www.wikidata.org/wiki/Q1338171","display_name":"Compensation (psychology)","level":2,"score":0.42890000343322754},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.41670000553131104},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.414000004529953},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.39980000257492065},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.3995000123977661},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3756999969482422},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.37209999561309814},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C34585555","wikidata":"https://www.wikidata.org/wiki/Q1368723","display_name":"Learning curve","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.26260000467300415},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.251800000667572}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11357066","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11357066","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.619829535484314,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G286870804","display_name":null,"funder_award_id":"2023QN06005","funder_id":"https://openalex.org/F4320322868","funder_display_name":"Natural Science Foundation of Inner Mongolia"},{"id":"https://openalex.org/G8805085442","display_name":null,"funder_award_id":"62362054","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322868","display_name":"Natural Science Foundation of Inner Mongolia","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2519210008","https://openalex.org/W2560647685","https://openalex.org/W3095948197","https://openalex.org/W4225130530","https://openalex.org/W4312769405","https://openalex.org/W4390874191","https://openalex.org/W4392904059","https://openalex.org/W4402727618"],"related_works":[],"abstract_inverted_index":{"Federated":[0],"learning":[1,18,23,74,129],"(FL)":[2],"enables":[3],"cross-institution":[4],"model":[5],"training":[6,105],"under":[7],"privacy":[8,38],"constraints":[9],"but":[10],"is":[11],"prone":[12],"to":[13,67,84,107],"catastrophic":[14],"forgetting":[15],"in":[16],"continual":[17,22,128],"scenarios.":[19],"Many":[20],"federated":[21,127],"(FCL)":[24],"methods":[25],"rely":[26],"on":[27,75,113],"rehearsal":[28],"(storing":[29],"or":[30],"generating":[31],"past":[32,55],"data),":[33],"which":[34],"conflicts":[35],"with":[36],"medical":[37,115],"requirements.":[39],"We":[40],"propose":[41,101],"FedOCP-LRAT,":[42],"a":[43,50,80,102],"rehearsal-free":[44],"FCL":[45],"framework.":[46],"The":[47],"server":[48],"constructs":[49],"global":[51],"important":[52],"subspace":[53],"from":[54,87],"tasks":[56,60],"and":[57,117],"preserves":[58],"prior":[59],"knowledge":[61,86],"by":[62,96],"constraining":[63],"new":[64,76],"parameter":[65],"updates":[66],"its":[68],"orthogonal":[69],"complement.":[70],"To":[71,90],"facilitate":[72],"the":[73,92,97,109],"tasks,":[77],"we":[78,100],"introduce":[79],"trainable":[81],"scaling":[82,98,110],"matrix":[83],"reuse":[85],"old":[88],"tasks.":[89],"reduce":[91],"communication":[93],"overhead":[94],"introduced":[95],"matrix,":[99],"low-rank":[103],"alternating":[104],"strategy":[106],"optimize":[108],"matrix.":[111],"Experiments":[112],"two":[114],"datasets":[116],"CIFAR100":[118],"show":[119],"consistent":[120],"improvements":[121],"over":[122],"strong":[123],"baselines,":[124],"enabling":[125],"privacy-compliant":[126],"without":[130],"retaining":[131],"historical":[132],"patient":[133],"data.":[134]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-30T00:00:00"}