{"id":"https://openalex.org/W4395665946","doi":"https://doi.org/10.1145/3625687.3625800","title":"FedINC: An Exemplar-Free Continual Federated Learning Framework with Small Labeled Data","display_name":"FedINC: An Exemplar-Free Continual Federated Learning Framework with Small Labeled Data","publication_year":2023,"publication_date":"2023-11-12","ids":{"openalex":"https://openalex.org/W4395665946","doi":"https://doi.org/10.1145/3625687.3625800"},"language":"en","primary_location":{"id":"doi:10.1145/3625687.3625800","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3625687.3625800","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3625687.3625800","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037386169","display_name":"Yongheng Deng","orcid":"https://orcid.org/0000-0003-3010-3812"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongheng Deng","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3010-3812","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101752867","display_name":"Sheng Yue","orcid":"https://orcid.org/0009-0001-3416-8181"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Yue","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0001-3416-8181","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067496010","display_name":"Tuowei Wang","orcid":"https://orcid.org/0009-0006-8272-8151"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tuowei Wang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0006-8272-8151","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017406233","display_name":"G. Y. Wang","orcid":"https://orcid.org/0009-0004-7468-5249"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanbo Wang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-7468-5249","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015419107","display_name":"Ju Ren","orcid":"https://orcid.org/0000-0003-2782-183X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ju Ren","raw_affiliation_strings":["Tsinghua University, Beijing, China","Zhongguancun Laboratory, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2782-183X","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Zhongguancun Laboratory, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069049205","display_name":"Yaoxue Zhang","orcid":"https://orcid.org/0000-0001-6717-461X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaoxue Zhang","raw_affiliation_strings":["Tsinghua University, Beijing, China","Zhongguancun Laboratory, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6717-461X","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Zhongguancun Laboratory, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"56","last_page":"69"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9994999766349792,"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":0.9994999766349792,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9857000112533569,"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":0.9761000275611877,"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":0.7863994836807251},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.4173227548599243},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3099214434623718}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7863994836807251},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.4173227548599243},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3099214434623718}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3625687.3625800","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3625687.3625800","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3625687.3625800","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3625687.3625800","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5185231776","display_name":null,"funder_award_id":"2022YFF0604502","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W2002427601","https://openalex.org/W2024922353","https://openalex.org/W2138621090","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2473930607","https://openalex.org/W2554863749","https://openalex.org/W2560647685","https://openalex.org/W2592962403","https://openalex.org/W2786446225","https://openalex.org/W2954929116","https://openalex.org/W2963588172","https://openalex.org/W2995139074","https://openalex.org/W2995191368","https://openalex.org/W3001197829","https://openalex.org/W3030364939","https://openalex.org/W3035060554","https://openalex.org/W3035501943","https://openalex.org/W3035524453","https://openalex.org/W3082248862","https://openalex.org/W3089562172","https://openalex.org/W3091002423","https://openalex.org/W3098511564","https://openalex.org/W3100779497","https://openalex.org/W3168149265","https://openalex.org/W3169555146","https://openalex.org/W3174401204","https://openalex.org/W3174868646","https://openalex.org/W3175771944","https://openalex.org/W3176547030","https://openalex.org/W3177095755","https://openalex.org/W3178686235","https://openalex.org/W3180392831","https://openalex.org/W3181161034","https://openalex.org/W3182158470","https://openalex.org/W3193756050","https://openalex.org/W3211384591","https://openalex.org/W3211781253","https://openalex.org/W4221159290","https://openalex.org/W4225130530","https://openalex.org/W4226006762","https://openalex.org/W4283032505","https://openalex.org/W4287812705","https://openalex.org/W4290755975","https://openalex.org/W4295411250","https://openalex.org/W4298113282","https://openalex.org/W4300410453","https://openalex.org/W4312769405","https://openalex.org/W4317926941","https://openalex.org/W6776700526"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4298221930","https://openalex.org/W2390279801","https://openalex.org/W2777914285","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4378677776","https://openalex.org/W3176937389"],"abstract_inverted_index":{"Federated":[0],"learning":[1,9,31,45,75,93,133],"(FL)":[2],"has":[3],"shown":[4],"great":[5],"promise":[6],"for":[7,73,127,180],"privacy-preserving":[8],"by":[10,109],"enabling":[11],"collaborative":[12],"training":[13],"on":[14,121],"decentralized":[15],"clients.":[16],"However,":[17],"in":[18,54,194],"realistic":[19],"FL":[20],"scenarios,":[21],"clients":[22],"often":[23],"collect":[24],"new":[25,46,76,138,166],"data":[26,53],"continuously,":[27],"join":[28],"or":[29],"exit":[30],"dynamically.":[32],"As":[33],"a":[34,88,99,130,144,155,176],"result,":[35],"the":[36,50,60,66,111,170,189,203],"global":[37,100,204],"model":[38,102],"tends":[39],"to":[40,97,135,149,159],"forget":[41],"old":[42,80,151,164],"knowledge":[43,77,139],"while":[44,168],"knowledge.":[47,81],"Meanwhile,":[48],"labeling":[49],"continuously":[51],"arriving":[52],"real-time":[55],"is":[56],"usually":[57],"challenging.":[58],"Therefore,":[59],"catastrophic":[61,114],"forgetting":[62,115],"problem":[63],"intertwined":[64],"with":[65,103,140],"label":[67],"deficiency":[68],"issue":[69],"poses":[70],"significant":[71],"challenges":[72],"both":[74,197],"and":[78,165,175,182,200],"consolidating":[79],"To":[82],"address":[83],"these":[84],"challenges,":[85],"we":[86,123],"develop":[87],"novel":[89],"exemplar-free":[90],"continual":[91],"federated":[92],"framework":[94],"named":[95],"FedINC,":[96,128],"learn":[98,137],"incremental":[101,184],"limited":[104,141],"labeled":[105,142],"data.":[106],"We":[107],"begin":[108],"excavating":[110],"cause":[112],"of":[113,172,192,196,202],"via":[116],"in-depth":[117],"empirical":[118],"studies.":[119],"Based":[120],"that,":[122],"introduce":[124],"targeted":[125],"mechanisms":[126],"including":[129],"hybrid":[131],"contrastive":[132],"mechanism":[134,148,158,179],"efficiently":[136],"data,":[143],"plastic":[145],"feature":[146,161],"regularization":[147,157],"preserve":[150],"task's":[152],"representation":[153],"space,":[154],"prototype-guided":[156],"mitigate":[160],"overlap":[162],"between":[163],"classes":[167],"aligning":[169],"features":[171],"non-iid":[173],"clients,":[174],"prototype":[177],"evolution":[178],"flexible":[181],"efficient":[183],"classification.":[185],"Extensive":[186],"experiments":[187],"demonstrate":[188],"superior":[190],"performance":[191],"FedINC":[193],"terms":[195],"convergence":[198],"speed":[199],"accuracy":[201],"model.":[205]},"counts_by_year":[{"year":2025,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
