{"id":"https://openalex.org/W4415708240","doi":"https://doi.org/10.1109/icme59968.2025.11209918","title":"Mitigating Knowledge Forgetting by Generative Knowledge Replay and Forgetting-aware Aggregation in Semi-Supervised Federated Learning","display_name":"Mitigating Knowledge Forgetting by Generative Knowledge Replay and Forgetting-aware Aggregation in Semi-Supervised Federated Learning","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415708240","doi":"https://doi.org/10.1109/icme59968.2025.11209918"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11209918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11209918","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 Multimedia and Expo (ICME)","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/A5100744267","display_name":"Hongquan Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongquan Liu","raw_affiliation_strings":["Fudan University,School of Computer Science,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Fudan University,School of Computer Science,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054723372","display_name":"Yixin Ren","orcid":"https://orcid.org/0000-0002-0084-4903"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yixin Ren","raw_affiliation_strings":["Fudan University,School of Computer Science,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Fudan University,School of Computer Science,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086316879","display_name":"Jihong Guan","orcid":"https://orcid.org/0000-0003-2313-7635"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jihong Guan","raw_affiliation_strings":["Tongji University,School of Computer Science and Technology,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Tongji University,School of Computer Science and Technology,Shanghai,China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017862559","display_name":"Shuigeng Zhou","orcid":"https://orcid.org/0000-0002-1949-2768"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuigeng Zhou","raw_affiliation_strings":["Fudan University,School of Computer Science,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Fudan University,School of Computer Science,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100744267"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17522265,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.32739999890327454,"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":0.32739999890327454,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.1453000009059906,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.11460000276565552,"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/leverage","display_name":"Leverage (statistics)","score":0.6990000009536743},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5167999863624573},{"id":"https://openalex.org/keywords/independent-and-identically-distributed-random-variables","display_name":"Independent and identically distributed random variables","score":0.5026000142097473},{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.4819999933242798},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4142000079154968},{"id":"https://openalex.org/keywords/complement","display_name":"Complement (music)","score":0.38339999318122864},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3799000084400177},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.37049999833106995},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.365200012922287}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.785099983215332},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6990000009536743},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5167999863624573},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.5026000142097473},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.4819999933242798},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42239999771118164},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4214000105857849},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4142000079154968},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.38339999318122864},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3799000084400177},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.365200012922287},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.36230000853538513},{"id":"https://openalex.org/C189645446","wikidata":"https://www.wikidata.org/wiki/Q350865","display_name":"Mirroring","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3481999933719635},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.34689998626708984},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33709999918937683},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3116999864578247},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.30160000920295715},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C71901391","wikidata":"https://www.wikidata.org/wiki/Q7126699","display_name":"Upload","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.25839999318122864},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.25619998574256897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11209918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11209918","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 Multimedia and Expo (ICME)","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":14,"referenced_works":["https://openalex.org/W2001610032","https://openalex.org/W2194775991","https://openalex.org/W2995022099","https://openalex.org/W3035682985","https://openalex.org/W4225482284","https://openalex.org/W4312462223","https://openalex.org/W4312869277","https://openalex.org/W4386071578","https://openalex.org/W4386075641","https://openalex.org/W4387846074","https://openalex.org/W4387968262","https://openalex.org/W4390871796","https://openalex.org/W4390873683","https://openalex.org/W4398765792"],"related_works":[],"abstract_inverted_index":{"Semi-supervised":[0],"federated":[1],"learning":[2],"(SSFL)":[3],"aims":[4],"to":[5,111],"leverage":[6],"the":[7,25,69,77,93,113],"vast":[8],"amount":[9,19],"of":[10,20,34,72,137],"unlabeled":[11],"data":[12,22,44,89,167],"distributed":[13,50],"across":[14,165],"clients":[15],"and":[16,42,48,53,75,97,155,172],"a":[17,31,88,126,142],"limited":[18],"labeled":[21],"held":[23],"by":[24,38,55,91],"central":[26],"server.":[27],"However,":[28],"SSFL":[29],"faces":[30],"tough":[32],"challenge":[33],"catastrophic":[35],"forgetting,":[36,139],"caused":[37],"discrepancies":[39],"between":[40,95],"local":[41,96,118,131],"global":[43,98,103,115,145],"distributions":[45],"in":[46,80,141],"non-independent":[47],"identically":[49],"(non-IID)":[51],"settings,":[52,168],"exacerbated":[54],"noisy":[56],"pseudo-labels.":[57,78],"To":[58],"deal":[59],"with":[60],"this":[61,81,85],"problem,":[62],"existing":[63],"methods":[64],"typically":[65],"focus":[66],"on":[67,134,151],"mitigating":[68],"adverse":[70],"effects":[71],"distribution":[73],"divergence":[74,94],"refining":[76],"Differently,":[79],"paper":[82],"we":[83,101,121],"tackle":[84],"problem":[86],"from":[87],"perspective":[90],"reducing":[92],"distributions.":[99],"Specifically,":[100],"propose":[102],"knowledge":[104,116,138],"generative":[105],"replay,":[106],"which":[107],"generates":[108],"synthetic":[109],"samples":[110],"complement":[112],"missing":[114],"during":[117],"training.":[119],"Additionally,":[120],"introduce":[122],"forgetting-aware":[123],"model":[124],"aggregation,":[125],"method":[127,161],"that":[128,159],"adaptively":[129],"re-weights":[130],"models":[132],"based":[133],"their":[135],"degree":[136],"resulting":[140],"more":[143],"robust":[144],"model.":[146],"We":[147],"conduct":[148],"extensive":[149],"experiments":[150],"widely-used":[152],"benchmark":[153],"datasets,":[154],"experimental":[156],"results":[157],"show":[158],"our":[160],"achieves":[162],"state-of-the-art":[163],"performance":[164],"various":[166],"validating":[169],"its":[170],"effectiveness":[171],"superiority.":[173],"The":[174],"code":[175],"will":[176],"be":[177],"available":[178],"at":[179],"https://github.com/lhq12/SemiFed.":[180]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-30T00:00:00"}