{"id":"https://openalex.org/W7164915878","doi":"https://doi.org/10.48550/arxiv.2606.15695","title":"When Generator Replay Degrades: Projected Rehearsal Orchestration for Heterogeneous Federated Class-Incremental Learning","display_name":"When Generator Replay Degrades: Projected Rehearsal Orchestration for Heterogeneous Federated Class-Incremental Learning","publication_year":2026,"publication_date":"2026-06-14","ids":{"openalex":"https://openalex.org/W7164915878","doi":"https://doi.org/10.48550/arxiv.2606.15695"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.15695","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15695","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.15695","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138723043","display_name":"Thinh T. H. Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Thinh T. H.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080642445","display_name":"Khoa D. Doan","orcid":"https://orcid.org/0000-0002-1610-8206"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Doan, Khoa D.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127778571","display_name":"Binh T. Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Binh T.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084289174","display_name":"Danh Le-Phuoc","orcid":"https://orcid.org/0000-0003-2480-9261"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le-Phuoc, Danh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129666703","display_name":"Kok-Seng Wong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wong, Kok-Seng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":null,"last_page":null},"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":0.7936999797821045,"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":0.7936999797821045,"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":0.053199999034404755,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.02459999918937683,"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/orchestration","display_name":"Orchestration","score":0.5989000201225281},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5558000206947327},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.534500002861023},{"id":"https://openalex.org/keywords/homogeneous","display_name":"Homogeneous","score":0.5230000019073486},{"id":"https://openalex.org/keywords/transfer","display_name":"Transfer (computing)","score":0.47429999709129333},{"id":"https://openalex.org/keywords/session","display_name":"Session (web analytics)","score":0.41830000281333923},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.39800000190734863}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8863999843597412},{"id":"https://openalex.org/C199168358","wikidata":"https://www.wikidata.org/wiki/Q3367000","display_name":"Orchestration","level":3,"score":0.5989000201225281},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5558000206947327},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.534500002861023},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.5230000019073486},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.47429999709129333},{"id":"https://openalex.org/C2779182362","wikidata":"https://www.wikidata.org/wiki/Q17126187","display_name":"Session (web analytics)","level":2,"score":0.41830000281333923},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.39800000190734863},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3700000047683716},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.36340001225471497},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.3481000065803528},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2935999929904938},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.15695","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15695","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.15695","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15695","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Federated":[0],"class-incremental":[1],"learning":[2],"(FCIL)":[3],"becomes":[4],"substantially":[5],"harder":[6],"when":[7,165],"clients":[8,96],"observe":[9],"different":[10,17],"label":[11],"subsets,":[12],"progress":[13],"through":[14,35],"tasks":[15],"at":[16],"stages,":[18],"and":[19,47,94,105,138,144,148,152,177],"provide":[20],"uneven":[21],"supervision":[22,175],"for":[23],"the":[24,80,92,127,132,214],"same":[25,81,128],"semantic":[26],"concepts.":[27],"Existing":[28],"FCIL":[29],"methods":[30,78],"often":[31],"preserve":[32],"old":[33,106],"knowledge":[34],"input-space":[36],"synthesis,":[37],"but":[38],"they":[39,172],"can":[40],"be":[41],"fragile":[42],"under":[43,79,155,174],"heterogeneous":[44,156],"task":[45],"streams":[46,157],"difficult":[48],"to":[49],"transfer":[50],"across":[51],"modalities.":[52],"To":[53,71,109],"alleviate":[54],"such":[55],"issues,":[56],"we":[57,75,114],"propose":[58],"PRO,":[59],"a":[60],"framework":[61],"that":[62,131,181,195],"replaces":[63],"synthetic":[64],"input":[65],"replay":[66,170,182,197],"with":[67,121,201,213],"projected":[68,89,107,209],"rehearsal":[69],"orchestration.":[70],"remove":[72],"external":[73],"pretraining,":[74],"evaluate":[76],"all":[77],"warmup.":[82],"After":[83],"this,":[84],"PRO":[85,120,147],"maintains":[86],"compact":[87],"class-level":[88],"memories":[90,210],"on":[91],"server":[93,133],"allows":[95],"perform":[97],"balanced":[98],"pseudo":[99],"multi-task":[100],"training":[101],"over":[102],"current":[103],"examples":[104],"memories.":[108],"handle":[110],"stronger":[111],"representation":[112],"drift,":[113],"further":[115,193],"introduce":[116],"PRO-MAX,":[117],"which":[118],"augments":[119],"neighborhood-weighted":[122],"memory":[123,139],"alignment":[124],"while":[125,158,205],"preserving":[126],"server-light":[129],"principle":[130],"only":[134],"aggregates":[135],"model":[136],"updates":[137],"statistics.":[140],"Across":[141],"image,":[142],"text,":[143],"graph":[145],"benchmarks,":[146],"PRO-MAX":[149],"improve":[150],"retention":[151],"final":[153],"utility":[154],"remaining":[159],"competitive":[160],"in":[161],"homogeneous":[162],"FCIL.":[163],"Even":[164],"baselines":[166],"are":[167],"given":[168],"expanded":[169],"budgets,":[171],"degrade":[173],"imbalance":[176],"stage":[178],"misalignment,":[179],"indicating":[180],"quantity":[183],"alone":[184],"does":[185],"not":[186],"resolve":[187],"replay-quality":[188],"failures.":[189],"Additional":[190],"weak-task":[191],"diagnostics":[192],"show":[194],"larger":[196,202],"mismatch":[198],"is":[199],"associated":[200],"downstream":[203],"degradation,":[204],"our":[206],"method":[207],"keeps":[208],"better":[211],"aligned":[212],"evolving":[215],"representation.":[216]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-17T00:00:00"}