{"id":"https://openalex.org/W4416920675","doi":"https://doi.org/10.1145/3737899.3768516","title":"FLATTN: Bridging Temporal Resolution Disparities via Attention Alignment in Federated Distillation for Human Activity Recognition","display_name":"FLATTN: Bridging Temporal Resolution Disparities via Attention Alignment in Federated Distillation for Human Activity Recognition","publication_year":2025,"publication_date":"2025-11-04","ids":{"openalex":"https://openalex.org/W4416920675","doi":"https://doi.org/10.1145/3737899.3768516"},"language":null,"primary_location":{"id":"doi:10.1145/3737899.3768516","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3737899.3768516","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Federated Learning and Edge AI for Privacy and Mobility","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/A5113613732","display_name":"Hui Shi","orcid":"https://orcid.org/0009-0005-1264-0317"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hanxiao Shi","raw_affiliation_strings":["Xidian University XIAN, China"],"affiliations":[{"raw_affiliation_string":"Xidian University XIAN, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101796598","display_name":"Qianru Wang","orcid":"https://orcid.org/0000-0002-1682-910X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qianru Wang","raw_affiliation_strings":["Xidian University XIAN, China"],"affiliations":[{"raw_affiliation_string":"Xidian University XIAN, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100700132","display_name":"Qingyang Li","orcid":"https://orcid.org/0000-0001-8514-1555"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingyang Li","raw_affiliation_strings":["Xidian University XIAN, China"],"affiliations":[{"raw_affiliation_string":"Xidian University XIAN, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100423964","display_name":"Hui Li","orcid":"https://orcid.org/0000-0003-2382-6289"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Li","raw_affiliation_strings":["Xidian University XIAN, China"],"affiliations":[{"raw_affiliation_string":"Xidian University XIAN, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048420839","display_name":"Jiangtao Cui","orcid":"https://orcid.org/0000-0001-5569-0780"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangtao Cui","raw_affiliation_strings":["Xidian University XIAN, China"],"affiliations":[{"raw_affiliation_string":"Xidian University XIAN, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5113613732"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39049903,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.5853000283241272,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.5853000283241272,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10812","display_name":"Human Pose and Action Recognition","score":0.26739999651908875,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.04230000078678131,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/bridging","display_name":"Bridging (networking)","score":0.704200029373169},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.6773999929428101},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5210000276565552},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.48240000009536743},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4433000087738037},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.44190001487731934},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.43369999527931213},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.42739999294281006},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.40130001306533813}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8011000156402588},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.704200029373169},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.6773999929428101},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5575000047683716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5519999861717224},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5210000276565552},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.48240000009536743},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4433000087738037},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.44190001487731934},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.43369999527931213},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.42739999294281006},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41679999232292175},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.40130001306533813},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.34779998660087585},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.32249999046325684},{"id":"https://openalex.org/C2776960227","wikidata":"https://www.wikidata.org/wiki/Q2586354","display_name":"Knowledge transfer","level":2,"score":0.3183000087738037},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.303600013256073},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3010999858379364},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C119666444","wikidata":"https://www.wikidata.org/wiki/Q5977280","display_name":"Temporal resolution","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3737899.3768516","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3737899.3768516","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Federated Learning and Edge AI for Privacy and Mobility","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":19,"referenced_works":["https://openalex.org/W2145343602","https://openalex.org/W2982157312","https://openalex.org/W2982242214","https://openalex.org/W2989289980","https://openalex.org/W2995022099","https://openalex.org/W3037913917","https://openalex.org/W4224227775","https://openalex.org/W4225576041","https://openalex.org/W4283396825","https://openalex.org/W4283796083","https://openalex.org/W4387460392","https://openalex.org/W4387717607","https://openalex.org/W4390664235","https://openalex.org/W4392901776","https://openalex.org/W4393156671","https://openalex.org/W4399053928","https://openalex.org/W4403598645","https://openalex.org/W4408280672","https://openalex.org/W6963732718"],"related_works":[],"abstract_inverted_index":{"Feature":[0],"Heterogeneity":[1],"in":[2,13,52],"federated":[3,97],"learning":[4,14],"has":[5],"become":[6],"a":[7,49,95,110],"key":[8],"issue":[9],"recently,":[10],"even":[11],"ubiquitous":[12],"from":[15],"distributed":[16],"time":[17,29,144],"series":[18,30],"data.":[19],"To":[20,87],"facilitate":[21],"knowledge":[22,120],"transfer":[23],"across":[24,84],"local":[25,85],"devices":[26,63],"with":[27,68,76,123],"heterogeneous":[28,62,124],"data,":[31],"recent":[32],"studies":[33],"have":[34],"made":[35],"efforts":[36],"to":[37,59,100,116],"learn":[38],"the":[39,44,53,81,89,103,106,119],"reasoning":[40],"process":[41],"by":[42],"reducing":[43,140],"discrepancy":[45],"among":[46],"features.":[47],"However,":[48],"new":[50],"challenge":[51],"human":[54,136],"activity":[55,137],"recognition":[56],"task":[57],"remains":[58],"be":[60],"discussed:":[61],"not":[64],"only":[65],"collect":[66,74],"data":[67,75],"different":[69,77],"feature":[70,82],"distributions":[71],"but":[72],"also":[73],"temporal":[78],"resolutions,":[79],"exacerbating":[80],"inconsistency":[83],"devices.":[86],"address":[88],"issue,":[90],"this":[91],"paper":[92],"proposes":[93],"FLATTN,":[94],"novel":[96],"distillation":[98],"method":[99],"effectively":[101],"enhance":[102],"generalization":[104],"of":[105],"global":[107],"model.":[108],"Meanwhile,":[109],"deformed":[111],"convolution":[112],"kernel":[113],"is":[114],"designed":[115],"efficiently":[117],"distill":[118],"for":[121,146],"models":[122],"resolution":[125],"input.":[126],"The":[127],"experiments":[128],"show":[129],"that":[130],"FLATTN":[131],"improves":[132],"accuracy":[133],"on":[134],"two":[135],"datasets":[138],"while":[139],"at":[141],"least":[142],"40.26%":[143],"costs":[145],"distillation.":[147]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-12-02T00:00:00"}
