{"id":"https://openalex.org/W4417300561","doi":"https://doi.org/10.48550/arxiv.2505.12245","title":"AFCL: Analytic Federated Continual Learning for Spatio-Temporal Invariance of Non-IID Data","display_name":"AFCL: Analytic Federated Continual Learning for Spatio-Temporal Invariance of Non-IID Data","publication_year":2025,"publication_date":"2025-05-18","ids":{"openalex":"https://openalex.org/W4417300561","doi":"https://doi.org/10.48550/arxiv.2505.12245"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2505.12245","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.12245","pdf_url":"https://arxiv.org/pdf/2505.12245","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2505.12245","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108535308","display_name":"Jianheng Tang","orcid":"https://orcid.org/0000-0002-4762-5943"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tang, Jianheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061256037","display_name":"Huiping Zhuang","orcid":"https://orcid.org/0000-0002-4612-5445"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhuang, Huiping","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108096795","display_name":"Jingyu He","orcid":"https://orcid.org/0000-0001-6033-2523"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Jingyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100521875","display_name":"Run He","orcid":"https://orcid.org/0009-0000-6450-9170"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Run","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043769191","display_name":"Jingchao Wang","orcid":"https://orcid.org/0000-0002-2535-8741"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jingchao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004976807","display_name":"Kejia Fan","orcid":"https://orcid.org/0000-0002-0236-759X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Kejia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100385833","display_name":"Yuxin Liu","orcid":"https://orcid.org/0000-0002-8632-2910"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Anfeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006546107","display_name":"Tian Wang","orcid":"https://orcid.org/0000-0003-4819-621X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Tian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055087680","display_name":"Leye Wang","orcid":"https://orcid.org/0000-0002-7627-8485"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Leye","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045305860","display_name":"Zhanxing Zhu","orcid":"https://orcid.org/0000-0002-2141-6553"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Zhanxing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040418528","display_name":"Shanghang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Shanghang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079301418","display_name":"Houbing Song","orcid":"https://orcid.org/0000-0003-2631-9223"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Houbing Herbert","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5082653046","display_name":"Yunhuai Liu","orcid":"https://orcid.org/0000-0002-1180-8078"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yunhuai","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":13,"corresponding_author_ids":["https://openalex.org/A5108535308"],"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":0.35280001163482666,"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":0.35280001163482666,"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":0.16099999845027924,"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":0.08810000121593475,"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/process","display_name":"Process (computing)","score":0.5041999816894531},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4830999970436096},{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.4684000015258789},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.45719999074935913},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"Distributed learning","score":0.42010000348091125},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.3682999908924103},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.35440000891685486},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.34220001101493835}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7531999945640564},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5041999816894531},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4830999970436096},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.4684000015258789},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.45719999074935913},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.42010000348091125},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3813000023365021},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37459999322891235},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.3682999908924103},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35370001196861267},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.33970001339912415},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.30469998717308044},{"id":"https://openalex.org/C84945661","wikidata":"https://www.wikidata.org/wiki/Q7366567","display_name":"Root cause","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C165136773","wikidata":"https://www.wikidata.org/wiki/Q1363179","display_name":"Single point of failure","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C2777851325","wikidata":"https://www.wikidata.org/wiki/Q7094102","display_name":"Online model","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2599000036716461}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2505.12245","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.12245","pdf_url":"https://arxiv.org/pdf/2505.12245","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2505.12245","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2505.12245","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2505.12245","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.12245","pdf_url":"https://arxiv.org/pdf/2505.12245","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Federated":[0,96],"Continual":[1,97],"Learning":[2,98],"(FCL)":[3],"enables":[4,115],"distributed":[5,33,167],"clients":[6,34,170],"to":[7,81,184],"collaboratively":[8],"train":[9],"a":[10,91,120],"global":[11,128,138],"model":[12,48,139,176],"from":[13,106],"online":[14,40,172],"task":[15],"streams":[16],"in":[17,73],"dynamic":[18],"real-world":[19],"scenarios.":[20],"However,":[21],"existing":[22],"FCL":[23],"methods":[24],"face":[25],"challenges":[26],"of":[27,55,69,79,152,161,177,186,196],"both":[28],"spatial":[29],"data":[30,37,43,165],"heterogeneity":[31,38,44],"among":[32],"and":[35,57,77,134,171,182,206],"temporal":[36],"across":[39,168,202],"tasks.":[41],"Such":[42],"significantly":[45],"degrades":[46],"the":[47,66,74,130,137,164,174,193],"performance":[49],"with":[50,118,140],"severe":[51],"spatial-temporal":[52],"catastrophic":[53],"forgetting":[54],"local":[56,111,169],"past":[58],"knowledge.":[59],"In":[60,110,127],"this":[61,70,87],"paper,":[62],"we":[63,89],"identify":[64],"that":[65,146,185],"root":[67],"cause":[68],"issue":[71],"lies":[72],"inherent":[75],"vulnerability":[76],"sensitivity":[78],"gradients":[80],"non-IID":[82,153],"data.":[83,154],"To":[84],"fundamentally":[85],"address":[86],"issue,":[88],"propose":[90],"gradient-free":[92],"method,":[93],"named":[94],"Analytic":[95],"(AFCL),":[99],"by":[100],"deriving":[101],"analytical":[102],"(i.e.,":[103],"closed-form)":[104],"solutions":[105],"frozen":[107],"extracted":[108],"features.":[109],"training,":[112],"our":[113,147,178,197],"AFCL":[114,148,179,198],"single-epoch":[116],"learning":[117],"only":[119],"lightweight":[121],"forward-propagation":[122],"process":[123],"for":[124],"each":[125],"client.":[126],"aggregation,":[129],"server":[131],"can":[132],"recursively":[133],"efficiently":[135],"update":[136],"single-round":[141],"aggregation.":[142],"Theoretical":[143],"analyses":[144],"validate":[145],"achieves":[149],"spatio-temporal":[150],"invariance":[151],"This":[155],"ideal":[156],"property":[157],"implies":[158],"that,":[159],"regardless":[160],"how":[162],"heterogeneous":[163],"are":[166],"tasks,":[173],"aggregated":[175],"remains":[180],"invariant":[181],"identical":[183],"centralized":[187],"joint":[188],"learning.":[189],"Extensive":[190],"experiments":[191],"show":[192],"consistent":[194],"superiority":[195],"over":[199],"state-of-the-art":[200],"baselines":[201],"various":[203],"benchmark":[204],"datasets":[205],"settings.":[207]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}