{"id":"https://openalex.org/W7124168874","doi":"https://doi.org/10.1109/icpads67057.2025.11322956","title":"SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning","display_name":"SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning","publication_year":2025,"publication_date":"2025-12-14","ids":{"openalex":"https://openalex.org/W7124168874","doi":"https://doi.org/10.1109/icpads67057.2025.11322956"},"language":null,"primary_location":{"id":"doi:10.1109/icpads67057.2025.11322956","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpads67057.2025.11322956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS)","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/A5123024674","display_name":"Nan Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210139618","display_name":"Shanghai Key Laboratory of Trustworthy Computing","ror":"https://ror.org/030qbr085","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210139618"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Nan Li","raw_affiliation_strings":["East China Normal University,MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application; Shanghai Key Laboratory of Trustworthy Computing,Shanghai,China,200062"],"affiliations":[{"raw_affiliation_string":"East China Normal University,MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application; Shanghai Key Laboratory of Trustworthy Computing,Shanghai,China,200062","institution_ids":["https://openalex.org/I4210139618"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078725373","display_name":"Xiaolu Wang","orcid":"https://orcid.org/0000-0002-5267-3464"},"institutions":[{"id":"https://openalex.org/I4210139618","display_name":"Shanghai Key Laboratory of Trustworthy Computing","ror":"https://ror.org/030qbr085","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210139618"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolu Wang","raw_affiliation_strings":["East China Normal University,MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application; Shanghai Key Laboratory of Trustworthy Computing,Shanghai,China,200062"],"affiliations":[{"raw_affiliation_string":"East China Normal University,MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application; Shanghai Key Laboratory of Trustworthy Computing,Shanghai,China,200062","institution_ids":["https://openalex.org/I4210139618"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053232298","display_name":"Xiao Du","orcid":"https://orcid.org/0000-0002-8479-6530"},"institutions":[{"id":"https://openalex.org/I4210139618","display_name":"Shanghai Key Laboratory of Trustworthy Computing","ror":"https://ror.org/030qbr085","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210139618"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Du","raw_affiliation_strings":["East China Normal University,MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application; Shanghai Key Laboratory of Trustworthy Computing,Shanghai,China,200062"],"affiliations":[{"raw_affiliation_string":"East China Normal University,MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application; Shanghai Key Laboratory of Trustworthy Computing,Shanghai,China,200062","institution_ids":["https://openalex.org/I4210139618"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chengcheng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chengcheng Wang","raw_affiliation_strings":["Naval Aviation University,Yantai,China,264001"],"affiliations":[{"raw_affiliation_string":"Naval Aviation University,Yantai,China,264001","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069784442","display_name":"Puyu Cai","orcid":"https://orcid.org/0000-0002-1368-8145"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Puyu Cai","raw_affiliation_strings":["New York University,Computer Science Department,New York,NY,USA,10012"],"affiliations":[{"raw_affiliation_string":"New York University,Computer Science Department,New York,NY,USA,10012","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122851854","display_name":"Ting Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210139618","display_name":"Shanghai Key Laboratory of Trustworthy Computing","ror":"https://ror.org/030qbr085","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210139618"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Wang","raw_affiliation_strings":["East China Normal University,MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application; Shanghai Key Laboratory of Trustworthy Computing,Shanghai,China,200062"],"affiliations":[{"raw_affiliation_string":"East China Normal University,MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application; Shanghai Key Laboratory of Trustworthy Computing,Shanghai,China,200062","institution_ids":["https://openalex.org/I4210139618"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5123024674"],"corresponding_institution_ids":["https://openalex.org/I4210139618"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.8291395,"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":0.9352999925613403,"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":0.9352999925613403,"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":0.013399999588727951,"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/T11719","display_name":"Data Quality and Management","score":0.007199999876320362,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.8306000232696533},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.7135000228881836},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6827999949455261},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.6403999924659729},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5819000005722046},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4246000051498413},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.39570000767707825}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8324999809265137},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.8306000232696533},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7135000228881836},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6827999949455261},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.6403999924659729},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5819000005722046},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5133000016212463},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4683000147342682},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4334000051021576},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4246000051498413},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.39570000767707825},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.3416999876499176},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.28369998931884766},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2768000066280365},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C2779965156","wikidata":"https://www.wikidata.org/wiki/Q5227350","display_name":"Data sharing","level":3,"score":0.27059999108314514},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpads67057.2025.11322956","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpads67057.2025.11322956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17","score":0.41273221373558044}],"awards":[{"id":"https://openalex.org/G4414558473","display_name":null,"funder_award_id":"24PJA02","funder_id":"https://openalex.org/F4320334593","funder_display_name":"Natural Sciences and Engineering Research Council of Canada"}],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2285660444","https://openalex.org/W2962851801","https://openalex.org/W2963363373","https://openalex.org/W2977090839","https://openalex.org/W2995022099","https://openalex.org/W3080934299","https://openalex.org/W3104631511","https://openalex.org/W3133504636","https://openalex.org/W3155963862","https://openalex.org/W3156024711","https://openalex.org/W3210103168","https://openalex.org/W4285603559","https://openalex.org/W4289546285","https://openalex.org/W4387544288"],"related_works":[],"abstract_inverted_index":{"Federated":[0,65],"Learning":[1],"(FL)":[2],"enables":[3],"collaborative":[4],"model":[5,33,40,115,129],"training":[6],"across":[7],"distributed":[8],"clients":[9,92,106],"while":[10],"preserving":[11],"data":[12,48,95],"privacy.":[13],"However,":[14],"conventional":[15],"FL":[16],"approaches":[17],"often":[18,49],"struggle":[19],"to":[20,38,54,102,132],"deliver":[21],"accurate":[22],"and":[23,97,111,128,147],"personalized":[24,151],"models":[25],"in":[26,113,149],"the":[27,108,142],"presence":[28],"of":[29,144],"non-IID":[30,137],"data.":[31],"Although":[32],"pruning":[34,146],"has":[35],"been":[36],"proposed":[37],"improve":[39],"adaptability,":[41],"existing":[42,133],"methods":[43],"relying":[44],"solely":[45],"on":[46,94,119],"local":[47],"yield":[50],"sub-optimal":[51],"sub-models":[52,110],"due":[53],"limited":[55],"task-specific":[56],"information.":[57],"To":[58],"address":[59],"this,":[60],"we":[61],"propose":[62],"SAFL":[63,84,124],"(Structure-Aware":[64],"Learning),":[66],"a":[67,86],"novel":[68],"framework":[69],"that":[70,123],"enhances":[71],"personalization":[72],"by":[73],"integrating":[74],"client":[75],"clustering":[76],"with":[77],"Similar":[78],"Client":[79],"Structure":[80],"Information":[81],"(SCSI)-guided":[82],"pruning.":[83],"adopts":[85],"two-stage":[87],"process:":[88],"it":[89],"first":[90],"clusters":[91],"based":[93],"similarity":[96],"uses":[98],"aggregated":[99],"structural":[100],"insights":[101],"guide":[103],"pruning;":[104],"then,":[105],"train":[107],"resulting":[109],"participate":[112],"heterogeneous":[114],"aggregation.":[116],"Extensive":[117],"experiments":[118],"benchmark":[120],"datasets":[121],"demonstrate":[122],"achieves":[125],"superior":[126],"accuracy":[127],"compactness":[130],"compared":[131],"methods,":[134],"particularly":[135],"under":[136],"settings.":[138],"These":[139],"results":[140],"highlight":[141],"effectiveness":[143],"structure-aware":[145],"collaboration":[148],"advancing":[150],"federated":[152],"learning.":[153]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-15T00:00:00"}
