{"id":"https://openalex.org/W4417231167","doi":"https://doi.org/10.1109/tnnls.2025.3639578","title":"SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning","display_name":"SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning","publication_year":2025,"publication_date":"2025-12-09","ids":{"openalex":"https://openalex.org/W4417231167","doi":"https://doi.org/10.1109/tnnls.2025.3639578","pmid":"https://pubmed.ncbi.nlm.nih.gov/41364583"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2025.3639578","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3639578","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5013265011","display_name":"Qiugang Zhan","orcid":"https://orcid.org/0000-0002-9474-0135"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiugang Zhan","raw_affiliation_strings":["Complex Laboratory of New Finance and Economics, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-9474-0135","affiliations":[{"raw_affiliation_string":"Complex Laboratory of New Finance and Economics, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China","institution_ids":["https://openalex.org/I204831749"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jinbo Cao","orcid":"https://orcid.org/0009-0001-7847-7568"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinbo Cao","raw_affiliation_strings":["Complex Laboratory of New Finance and Economics, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China"],"raw_orcid":"https://orcid.org/0009-0001-7847-7568","affiliations":[{"raw_affiliation_string":"Complex Laboratory of New Finance and Economics, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China","institution_ids":["https://openalex.org/I204831749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029314634","display_name":"Xiurui Xie","orcid":"https://orcid.org/0000-0002-3720-4379"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiurui Xie","raw_affiliation_strings":["Laboratory of Intelligent Collaborative Computing, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-3720-4379","affiliations":[{"raw_affiliation_string":"Laboratory of Intelligent Collaborative Computing, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057282687","display_name":"Huajin Tang","orcid":"https://orcid.org/0000-0002-9857-1543"},"institutions":[{"id":"https://openalex.org/I168879160","display_name":"Zhejiang University of Science and Technology","ror":"https://ror.org/05mx0wr29","country_code":"CN","type":"education","lineage":["https://openalex.org/I168879160"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huajin Tang","raw_affiliation_strings":["School of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-9857-1543","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I168879160"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031886937","display_name":"Malu Zhang","orcid":"https://orcid.org/0000-0002-2345-0974"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Malu Zhang","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-2345-0974","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038425463","display_name":"Shantian Yang","orcid":"https://orcid.org/0000-0003-2436-0580"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shantian Yang","raw_affiliation_strings":["Complex Laboratory of New Finance and Economics, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Complex Laboratory of New Finance and Economics, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China","institution_ids":["https://openalex.org/I204831749"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003393482","display_name":"Guisong Liu","orcid":"https://orcid.org/0000-0003-2360-0466"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guisong Liu","raw_affiliation_strings":["Complex Laboratory of New Finance and Economics, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-2360-0466","affiliations":[{"raw_affiliation_string":"Complex Laboratory of New Finance and Economics, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China","institution_ids":["https://openalex.org/I204831749"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.396,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.87517761,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"37","issue":"6","first_page":"2757","last_page":"2769"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.4779999852180481,"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":0.4779999852180481,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.1932000070810318,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11896","display_name":"Opportunistic and Delay-Tolerant Networks","score":0.04340000078082085,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.6272000074386597},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.618399977684021},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5085999965667725},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4814000129699707},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.43959999084472656},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.37720000743865967},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.3562000095844269},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.3409000039100647},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.3334999978542328}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8087999820709229},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6272000074386597},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.618399977684021},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5085999965667725},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4814000129699707},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.43959999084472656},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3955000042915344},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3856000006198883},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.37720000743865967},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.3562000095844269},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3521000146865845},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.3409000039100647},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.323199987411499},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.29989999532699585},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29350000619888306},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.2660999894142151},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.26159998774528503},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.25780001282691956},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2025.3639578","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3639578","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:41364583","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41364583","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1541418386","display_name":null,"funder_award_id":"JBK202511077","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G2979803263","display_name":null,"funder_award_id":"JBK2406079","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G60673156","display_name":null,"funder_award_id":"62376228","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G851538271","display_name":null,"funder_award_id":"2023-JB00-00016-GX","funder_id":"https://openalex.org/F4320336736","funder_display_name":"Chengdu Science and Technology Program"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null},{"id":"https://openalex.org/F4320336736","display_name":"Chengdu Science and Technology Program","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,136,149],"spiking":[1,42,48,91,160],"federated":[2,92],"learning":[3,9],"(FL)":[4],"is":[5],"an":[6],"emerging":[7],"distributed":[8],"paradigm":[10],"that":[11,153],"allows":[12],"resource-constrained":[13],"devices":[14],"to":[15,96,100],"train":[16],"collaboratively":[17],"at":[18],"low":[19],"power":[20],"consumption":[21],"without":[22],"exchanging":[23],"local":[24,121,129],"data.":[25],"It":[26],"takes":[27],"advantage":[28],"of":[29,65,74],"both":[30],"the":[31,38,70,75,94,101,108,114,126,133,154,157],"privacy":[32],"computation":[33],"property":[34],"in":[35,41,128],"FL":[36,49,161],"and":[37,72,119,144,163],"energy":[39],"efficiency":[40],"neural":[43],"networks":[44],"(SNNs).":[45],"However,":[46],"existing":[47,158],"methods":[50,162],"employ":[51],"a":[52,83],"random":[53],"selection":[54,88],"approach":[55],"for":[56,90],"client":[57,61,87,109],"aggregation,":[58],"assuming":[59],"unbiased":[60],"participation.":[62],"This":[63],"neglect":[64],"statistical":[66],"heterogeneity":[67],"significantly":[68],"affects":[69],"convergence":[71],"precision":[73],"global":[76,102,134],"model.":[77,135],"In":[78],"this":[79],"work,":[80],"we":[81],"propose":[82],"credit":[84],"assignment-based":[85],"active":[86],"strategy":[89],"learning,":[93],"SFedCA,":[95],"aggregate":[97],"clients":[98],"contributing":[99],"sample":[103],"distribution":[104,131,146],"balance":[105],"judiciously.":[106],"Specifically,":[107],"credits":[110],"are":[111,139],"assigned":[112],"by":[113],"firing":[115],"intensity":[116],"state":[117],"before":[118],"after":[120],"model":[122],"training,":[123],"which":[124],"reflects":[125],"difference":[127],"data":[130],"from":[132],"comprehensive":[137],"experiments":[138],"conducted":[140],"on":[141],"various":[142],"non-identical":[143],"independent":[145],"(non-IID)":[147],"scenarios.":[148],"experimental":[150],"results":[151],"demonstrate":[152],"SFedCA":[155],"outperforms":[156],"state-of-the-art":[159],"requires":[164],"fewer":[165],"communication":[166],"rounds.":[167]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-04T06:14:18.571774","created_date":"2025-12-10T00:00:00"}