{"id":"https://openalex.org/W4416252241","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229222","title":"Hierarchical Knowledge Structuring for Effective Federated Learning in Heterogeneous Environments","display_name":"Hierarchical Knowledge Structuring for Effective Federated Learning in Heterogeneous Environments","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416252241","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229222"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11229222","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229222","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5120288546","display_name":"Wai Fong Tam","orcid":null},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Wai Fong Tam","raw_affiliation_strings":["Queen Mary University of London,London,United Kingdom"],"affiliations":[{"raw_affiliation_string":"Queen Mary University of London,London,United Kingdom","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041574741","display_name":"Qilei Li","orcid":"https://orcid.org/0000-0002-9675-9016"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Qilei Li","raw_affiliation_strings":["Queen Mary University of London,London,United Kingdom"],"affiliations":[{"raw_affiliation_string":"Queen Mary University of London,London,United Kingdom","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070017771","display_name":"Ahmed M. Abdelmoniem","orcid":"https://orcid.org/0000-0002-1374-1882"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ahmed M. Abdelmoniem","raw_affiliation_strings":["Queen Mary University of London,London,United Kingdom"],"affiliations":[{"raw_affiliation_string":"Queen Mary University of London,London,United Kingdom","institution_ids":["https://openalex.org/I166337079"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5120288546"],"corresponding_institution_ids":["https://openalex.org/I166337079"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19519297,"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":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9553999900817871,"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":0.9553999900817871,"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":0.004999999888241291,"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":0.004000000189989805,"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/federated-learning","display_name":"Federated learning","score":0.7368999719619751},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6456999778747559},{"id":"https://openalex.org/keywords/structuring","display_name":"Structuring","score":0.5493999719619751},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5141000151634216},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5027999877929688},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.489300012588501},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.44690001010894775},{"id":"https://openalex.org/keywords/data-driven","display_name":"Data-driven","score":0.3668999969959259}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.781000018119812},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7368999719619751},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6456999778747559},{"id":"https://openalex.org/C2775945657","wikidata":"https://www.wikidata.org/wiki/Q381442","display_name":"Structuring","level":2,"score":0.5493999719619751},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5141000151634216},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5027999877929688},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.489300012588501},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.44690001010894775},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4207000136375427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39250001311302185},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.3668999969959259},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.35740000009536743},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C2776604539","wikidata":"https://www.wikidata.org/wiki/Q6423395","display_name":"Knowledge sharing","level":2,"score":0.34369999170303345},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.32820001244544983},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C138020889","wikidata":"https://www.wikidata.org/wiki/Q2349659","display_name":"Collaborative learning","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C127759330","wikidata":"https://www.wikidata.org/wiki/Q637416","display_name":"Codebook","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.28630000352859497},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.27129998803138733},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26660001277923584},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11229222","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229222","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":25,"referenced_works":["https://openalex.org/W3042621011","https://openalex.org/W3047304572","https://openalex.org/W3133814152","https://openalex.org/W3160015479","https://openalex.org/W3182158470","https://openalex.org/W3186051974","https://openalex.org/W3189545149","https://openalex.org/W3196371845","https://openalex.org/W4212815771","https://openalex.org/W4292829107","https://openalex.org/W4312592506","https://openalex.org/W4312869277","https://openalex.org/W4366112239","https://openalex.org/W4379108797","https://openalex.org/W4383503597","https://openalex.org/W4386075786","https://openalex.org/W4387869591","https://openalex.org/W4389454722","https://openalex.org/W4391528168","https://openalex.org/W4392904197","https://openalex.org/W4394849811","https://openalex.org/W4394923525","https://openalex.org/W4396843697","https://openalex.org/W4403636050","https://openalex.org/W4403864811"],"related_works":[],"abstract_inverted_index":{"Federated":[0],"learning":[1,19,146],"enables":[2],"collaborative":[3],"model":[4,176],"training":[5,142,164],"across":[6,55,172],"distributed":[7],"entities":[8],"while":[9],"maintaining":[10],"individual":[11,79],"data":[12,54,64],"privacy.":[13],"A":[14],"key":[15],"challenge":[16],"in":[17,61,154,162],"federated":[18],"is":[20,124,170],"balancing":[21],"the":[22,32,50,59,62,68,83,128],"personalization":[23],"of":[24,53,85],"models":[25],"for":[26,31],"local":[27,86,136,141],"clients":[28,57,80],"with":[29,148],"generalization":[30,150],"global":[33,129,149],"model.":[34],"Recent":[35],"efforts":[36],"leverage":[37],"logit-based":[38],"knowledge":[39,73,160],"aggregation":[40],"and":[41,58,81,158,175],"distillation":[42],"to":[43,49,76,78,108,115,126,131,135,143],"overcome":[44],"these":[45],"issues.":[46],"However,":[47],"due":[48],"non-IID":[51],"nature":[52],"diverse":[56],"imbalance":[60],"client\u2019s":[63],"distribution,":[65],"directly":[66],"aggregating":[67],"logits":[69,103,110],"often":[70],"produces":[71],"biased":[72],"that":[74,100],"fails":[75],"apply":[77],"obstructs":[82],"convergence":[84],"training.":[87],"To":[88],"solve":[89],"this":[90],"issue,":[91],"we":[92],"propose":[93],"a":[94,105],"Hierarchical":[95],"Knowledge":[96],"Structuring":[97],"(HKS)":[98],"framework":[99],"formulates":[101],"sample":[102],"into":[104],"multi-granularity":[106,133],"codebook":[107],"represent":[109],"from":[111],"personalized":[112],"per-sample":[113],"insights":[114],"globalized":[116],"per-class":[117],"knowledge.":[118],"The":[119,166],"unsupervised":[120],"bottom-up":[121],"clustering":[122],"method":[123],"leveraged":[125],"enable":[127],"server":[130],"provide":[132],"responses":[134,139],"clients.":[137],"These":[138],"allow":[140],"integrate":[144],"supervised":[145],"objectives":[147],"constraints,":[151],"which":[152],"results":[153],"more":[155],"robust":[156],"representations":[157],"improved":[159],"sharing":[161],"subsequent":[163],"rounds.":[165],"proposed":[167],"framework\u2019s":[168],"effectiveness":[169],"validated":[171],"various":[173],"benchmarks":[174],"architectures.":[177]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
