{"id":"https://openalex.org/W4413881432","doi":"https://doi.org/10.3390/bdcc9090226","title":"FedPLDSE: Submodel Extraction for Federated Learning in Heterogeneous Smart City Devices","display_name":"FedPLDSE: Submodel Extraction for Federated Learning in Heterogeneous Smart City Devices","publication_year":2025,"publication_date":"2025-08-30","ids":{"openalex":"https://openalex.org/W4413881432","doi":"https://doi.org/10.3390/bdcc9090226"},"language":"en","primary_location":{"id":"doi:10.3390/bdcc9090226","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc9090226","pdf_url":"https://www.mdpi.com/2504-2289/9/9/226/pdf?version=1756543629","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-2289/9/9/226/pdf?version=1756543629","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119498942","display_name":"Xiaochi Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaochi Hou","raw_affiliation_strings":["College of Computer Science, Inner Mongolia University, Hohhot 010021, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Inner Mongolia University, Hohhot 010021, China","institution_ids":["https://openalex.org/I2722730"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101606198","display_name":"Zhigang Wang","orcid":"https://orcid.org/0000-0001-6608-9157"},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhigang Wang","raw_affiliation_strings":["College of Computer Science, Inner Mongolia University, Hohhot 010021, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Inner Mongolia University, Hohhot 010021, China","institution_ids":["https://openalex.org/I2722730"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115591523","display_name":"Xinhao Wang","orcid":"https://orcid.org/0009-0002-5639-9914"},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinhao Wang","raw_affiliation_strings":["College of Computer Science, Inner Mongolia University, Hohhot 010021, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Inner Mongolia University, Hohhot 010021, China","institution_ids":["https://openalex.org/I2722730"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009946754","display_name":"Junfeng Zhao","orcid":"https://orcid.org/0000-0002-1268-5006"},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junfeng Zhao","raw_affiliation_strings":["College of Computer Science, Inner Mongolia University, Hohhot 010021, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Inner Mongolia University, Hohhot 010021, China","institution_ids":["https://openalex.org/I2722730"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5119498942"],"corresponding_institution_ids":["https://openalex.org/I2722730"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13219465,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":"9","first_page":"226","last_page":"226"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"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":0.9998999834060669,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9972000122070312,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5812104940414429},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.5761225819587708},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.4269736409187317},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.21162328124046326},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.05332198739051819}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5812104940414429},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.5761225819587708},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.4269736409187317},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21162328124046326},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.05332198739051819},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/bdcc9090226","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc9090226","pdf_url":"https://www.mdpi.com/2504-2289/9/9/226/pdf?version=1756543629","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:9c59d1952818408fbda046e6166355b5","is_oa":true,"landing_page_url":"https://doaj.org/article/9c59d1952818408fbda046e6166355b5","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Big Data and Cognitive Computing, Vol 9, Iss 9, p 226 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/bdcc9090226","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc9090226","pdf_url":"https://www.mdpi.com/2504-2289/9/9/226/pdf?version=1756543629","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4413881432.pdf","grobid_xml":"https://content.openalex.org/works/W4413881432.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W2095705004","https://openalex.org/W2194775991","https://openalex.org/W2883059862","https://openalex.org/W2896422817","https://openalex.org/W2978709794","https://openalex.org/W2995022099","https://openalex.org/W2996845627","https://openalex.org/W3011766871","https://openalex.org/W3014371581","https://openalex.org/W3019945581","https://openalex.org/W3033988836","https://openalex.org/W3035453001","https://openalex.org/W3083662943","https://openalex.org/W3094163844","https://openalex.org/W3100779497","https://openalex.org/W3106047871","https://openalex.org/W3106356772","https://openalex.org/W3106416029","https://openalex.org/W3136022984","https://openalex.org/W3141518839","https://openalex.org/W3155912831","https://openalex.org/W3159080474","https://openalex.org/W3175729092","https://openalex.org/W3178709966","https://openalex.org/W3202088367","https://openalex.org/W3211786132","https://openalex.org/W3213321731","https://openalex.org/W4283837358","https://openalex.org/W4285218268","https://openalex.org/W4285601829","https://openalex.org/W4287121309","https://openalex.org/W4287322665","https://openalex.org/W4292787311","https://openalex.org/W4310822417","https://openalex.org/W4311624935","https://openalex.org/W4386083155","https://openalex.org/W4386879292","https://openalex.org/W4393160503","https://openalex.org/W4401857167"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4298221930","https://openalex.org/W2390279801","https://openalex.org/W2777914285","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W3176937389","https://openalex.org/W4408069290"],"abstract_inverted_index":{"Federated":[0],"learning":[1],"enables":[2],"collaborative":[3],"model":[4,119,176],"training":[5,86],"across":[6],"distributed":[7],"devices":[8,25,42],"while":[9,40,154],"preserving":[10],"data":[11],"privacy.":[12],"However,":[13],"in":[14,69,81],"real-world":[15],"environments":[16],"such":[17],"as":[18],"smart":[19],"cities,":[20],"heterogeneous":[21],"and":[22,33,78,112,134,174,182,187,199],"resource-constrained":[23],"edge":[24],"often":[26],"render":[27],"existing":[28,101],"methods":[29,102],"impractical.":[30],"Low-power":[31],"sensors":[32],"cameras":[34],"struggle":[35],"to":[36,74,93,97,167],"complete":[37],"full-model":[38],"training,":[39],"high-performance":[41],"remain":[43],"idly":[44],"waiting":[45],"for":[46,108,137],"others.":[47],"Knowledge":[48],"distillation":[49],"approaches":[50],"rely":[51],"on":[52,147,179,194],"public":[53],"datasets":[54],"that":[55],"are":[56],"rarely":[57],"available":[58],"or":[59],"poorly":[60],"aligned":[61],"with":[62,190],"urban":[63,83],"data,":[64],"which":[65],"limits":[66],"their":[67,98],"effectiveness":[68],"deployment.":[70],"These":[71],"limitations":[72],"lead":[73],"inefficiencies,":[75],"unstable":[76],"convergence,":[77,189],"poor":[79],"adaptability":[80],"diverse":[82],"networks.":[84],"Partial":[85],"alleviates":[87],"some":[88],"challenges":[89],"by":[90],"allowing":[91],"clients":[92],"train":[94],"submodels":[95],"tailored":[96],"capacity,":[99],"but":[100],"still":[103],"incur":[104],"high":[105,202],"computational":[106],"costs":[107],"identifying":[109,156],"important":[110],"parameters":[111],"suffer":[113],"from":[114],"uneven":[115],"parameter":[116,142],"updates,":[117],"reducing":[118,152],"effectiveness.":[120],"To":[121],"address":[122],"these":[123],"challenges,":[124],"we":[125],"propose":[126],"Parameter-Level":[127],"Dynamic":[128],"Submodel":[129],"Extraction":[130],"(PLDSE),":[131],"a":[132,148,163],"lightweight":[133],"adaptive":[135],"framework":[136],"federated":[138],"learning.":[139],"PLDSE":[140,191],"estimates":[141],"importance":[143],"using":[144],"gradient-based":[145],"scores":[146],"server-side":[149],"validation":[150],"set,":[151],"overhead":[153],"accurately":[155],"critical":[157],"parameters.":[158],"In":[159],"addition,":[160],"it":[161],"integrates":[162],"rolling":[164],"scheduling":[165],"mechanism":[166],"rotate":[168],"unselected":[169],"parameters,":[170],"ensuring":[171],"full":[172],"coverage":[173],"consistent":[175],"updates.":[177],"Experiments":[178],"CIFAR-10,":[180],"CIFAR-100,":[181],"Fashion-MNIST":[183],"demonstrate":[184],"superior":[185],"accuracy":[186],"faster":[188],"achieving":[192],"62.82%":[193],"CIFAR-100":[195],"under":[196,201],"low":[197],"heterogeneity":[198],"61.51%":[200],"heterogeneity,":[203],"outperforming":[204],"prior":[205],"methods.":[206]},"counts_by_year":[],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}