{"id":"https://openalex.org/W7138401831","doi":"https://doi.org/10.48550/arxiv.2603.14956","title":"SFedHIFI: Fire Rate-Based Heterogeneous Information Fusion for Spiking Federated Learning","display_name":"SFedHIFI: Fire Rate-Based Heterogeneous Information Fusion for Spiking Federated Learning","publication_year":2026,"publication_date":"2026-03-16","ids":{"openalex":"https://openalex.org/W7138401831","doi":"https://doi.org/10.48550/arxiv.2603.14956"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.14956","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14956","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":null,"license_id":null,"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.2603.14956","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129695622","display_name":"Ran Tao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tao, Ran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129703904","display_name":"Qiugang Zhan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhan, Qiugang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038425463","display_name":"Shantian Yang","orcid":"https://orcid.org/0000-0003-2436-0580"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Shantian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129644819","display_name":"Xiurui Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Xiurui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129708860","display_name":"Qi Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Qi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129678858","display_name":"Guisong Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Guisong","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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.6218000054359436,"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"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.6218000054359436,"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":0.07580000162124634,"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"}},{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.06780000030994415,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.7113000154495239},{"id":"https://openalex.org/keywords/heterogeneous-network","display_name":"Heterogeneous network","score":0.5282999873161316},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.5200999975204468},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.45969998836517334},{"id":"https://openalex.org/keywords/symmetric-multiprocessor-system","display_name":"Symmetric multiprocessor system","score":0.4449000060558319},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.40149998664855957},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.37439998984336853}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.798799991607666},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7113000154495239},{"id":"https://openalex.org/C158207573","wikidata":"https://www.wikidata.org/wiki/Q5747224","display_name":"Heterogeneous network","level":4,"score":0.5282999873161316},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.5200999975204468},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.48260000348091125},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.45969998836517334},{"id":"https://openalex.org/C172430144","wikidata":"https://www.wikidata.org/wiki/Q17111997","display_name":"Symmetric multiprocessor system","level":2,"score":0.4449000060558319},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.40149998664855957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37770000100135803},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.37439998984336853},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.36169999837875366},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.34389999508857727},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32429999113082886},{"id":"https://openalex.org/C2982962833","wikidata":"https://www.wikidata.org/wiki/Q17092450","display_name":"Information fusion","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29660001397132874},{"id":"https://openalex.org/C82578977","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Data aggregator","level":3,"score":0.2824999988079071},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C81147070","wikidata":"https://www.wikidata.org/wiki/Q1172449","display_name":"Encapsulation (networking)","level":2,"score":0.2750999927520752}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.14956","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14956","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.14956","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14956","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.7425888180732727,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Spiking":[0,13,78],"Federated":[1,79],"Learning":[2,80],"(SFL)":[3],"has":[4],"been":[5],"widely":[6],"studied":[7],"with":[8,82,103,152,160],"the":[9,34,42,109,125],"energy":[10,158],"efficiency":[11],"of":[12,36,60,98,120,127],"Neural":[14],"Networks":[15],"(SNNs).":[16],"However,":[17],"existing":[18],"SFL":[19,51],"methods":[20],"require":[21],"model":[22],"homogeneity":[23],"and":[24],"assume":[25],"all":[26,147],"clients":[27,55,102],"have":[28],"sufficient":[29],"computational":[30],"resources,":[31],"resulting":[32],"in":[33,46,165],"exclusion":[35],"some":[37],"resource-constrained":[38],"clients.":[39],"To":[40,70],"address":[41],"prevalent":[43],"system":[44],"heterogeneity":[45],"real-world":[47],"scenarios,":[48],"enabling":[49],"heterogeneous":[50,104,111,143],"systems":[52],"that":[53,138],"allow":[54],"to":[56,94],"adaptively":[57],"deploy":[58,95],"models":[59,97,119],"different":[61,121],"scales":[62],"based":[63],"on":[64,101,107,133],"their":[65],"local":[66,129],"resources":[67],"is":[68],"crucial.":[69],"this":[71],"end,":[72],"we":[73],"introduce":[74],"SFedHIFI,":[75],"a":[76,162],"novel":[77],"framework":[81],"Fire":[83],"Rate-Based":[84],"Heterogeneous":[85],"Information":[86],"Fusion.":[87],"Specifically,":[88],"SFedHIFI":[89,139],"employs":[90],"channel-wise":[91],"matrix":[92],"decomposition":[93],"SNN":[96],"adaptive":[99],"complexity":[100],"resources.":[105],"Building":[106],"this,":[108],"proposed":[110],"information":[112],"fusion":[113],"module":[114],"enables":[115],"cross-scale":[116],"aggregation":[117],"among":[118],"widths,":[122],"thereby":[123],"enhancing":[124],"utilization":[126],"diverse":[128],"knowledge.":[130],"Extensive":[131],"experiments":[132],"three":[134,148],"public":[135],"benchmarks":[136],"demonstrate":[137],"can":[140],"effectively":[141],"enable":[142],"SFL,":[144],"consistently":[145],"outperforming":[146],"baseline":[149],"methods.":[150],"Compared":[151],"ANN-based":[153],"FL,":[154],"it":[155],"achieves":[156],"significant":[157],"savings":[159],"only":[161],"marginal":[163],"trade-off":[164],"accuracy.":[166]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-18T00:00:00"}
