{"id":"https://openalex.org/W4403792029","doi":"https://doi.org/10.1145/3664647.3681158","title":"Adaptive Hierarchical Aggregation for Federated Object Detection","display_name":"Adaptive Hierarchical Aggregation for Federated Object Detection","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403792029","doi":"https://doi.org/10.1145/3664647.3681158"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3681158","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681158","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","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/A5111367998","display_name":"Renxu Jia","orcid":"https://orcid.org/0009-0002-1621-3230"},"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":"Ruofan Jia","raw_affiliation_strings":["Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0009-0002-1621-3230","affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052163069","display_name":"Weiying Xie","orcid":"https://orcid.org/0000-0001-8310-024X"},"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":"Weiying Xie","raw_affiliation_strings":["Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0001-8310-024X","affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007285444","display_name":"Jie Lei","orcid":"https://orcid.org/0000-0003-0851-6565"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jie Lei","raw_affiliation_strings":["University of Technology Sydney, Sydney, Australia"],"raw_orcid":"https://orcid.org/0000-0003-0851-6565","affiliations":[{"raw_affiliation_string":"University of Technology Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067798266","display_name":"Yunsong Li","orcid":"https://orcid.org/0000-0002-0234-6270"},"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":"Yunsong Li","raw_affiliation_strings":["Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-0234-6270","affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4375,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63593735,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3732","last_page":"3740"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987000226974487,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9987000226974487,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.996999979019165,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9919999837875366,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7784281373023987},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4931713938713074},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4653658866882324},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.41115036606788635},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2359236776828766},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.13121876120567322}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7784281373023987},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4931713938713074},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4653658866882324},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.41115036606788635},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2359236776828766},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.13121876120567322}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3681158","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681158","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1861492603","https://openalex.org/W2005368619","https://openalex.org/W2031489346","https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2133665775","https://openalex.org/W2525579820","https://openalex.org/W2795807997","https://openalex.org/W2962766617","https://openalex.org/W2963037989","https://openalex.org/W2997747012","https://openalex.org/W3029897895","https://openalex.org/W3035453001","https://openalex.org/W3037913917","https://openalex.org/W3038022836","https://openalex.org/W3096609285","https://openalex.org/W3169044395","https://openalex.org/W3193972328","https://openalex.org/W3206772271","https://openalex.org/W3206845050","https://openalex.org/W4221156340","https://openalex.org/W4246877010","https://openalex.org/W4246999471","https://openalex.org/W4249914127","https://openalex.org/W4312231739","https://openalex.org/W4320018442","https://openalex.org/W4377862233","https://openalex.org/W4386076325","https://openalex.org/W4386160388","https://openalex.org/W4387706025","https://openalex.org/W4387717607","https://openalex.org/W4387968397","https://openalex.org/W4388787650","https://openalex.org/W4390874575","https://openalex.org/W6759238902","https://openalex.org/W6893711219"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W4292830139","https://openalex.org/W4319309705"],"abstract_inverted_index":{"In":[0,53],"practical":[1],"object":[2,42,47,98],"detection":[3],"scenarios,":[4],"distributed":[5],"data":[6,35],"and":[7,50,123,129,176],"stringent":[8],"privacy":[9],"protections":[10],"significantly":[11],"limit":[12],"the":[13,32,90,95,119,131,134,143],"feasibility":[14],"of":[15,34,115,133],"traditional":[16],"centralized":[17],"training":[18],"methods.":[19],"Federated":[20],"learning":[21,60],"(FL)":[22],"emerges":[23],"as":[24],"a":[25,57,158,171],"promising":[26],"solution":[27],"to":[28,40,68,110,153,165],"this":[29],"dilemma.":[30],"Nonetheless,":[31],"issue":[33],"heterogeneity":[36],"introduces":[37],"distinct":[38],"challenges":[39],"federated":[41,59],"detection,":[43],"evident":[44],"in":[45,75],"diminished":[46],"perception,":[48],"classification":[49,122],"localization":[51,124],"abilities.":[52],"response,":[54],"we":[55,156],"introduce":[56],"task-driven":[58],"methodology,":[61],"dubbed":[62],"Adaptive":[63],"Hierarchical":[64],"Aggregation":[65,84],"(FedAHA),":[66],"tailored":[67],"overcome":[69],"these":[70],"obstacles.":[71],"Our":[72,139],"algorithm":[73],"unfolds":[74],"two":[76,136],"strategic":[77],"phases":[78],"from":[79,151],"shallow-to-deep":[80],"layers:":[81],"(1)":[82],"Structure-aware":[83],"(SAA)":[85],"aligns":[86],"feature":[87],"extractors":[88],"during":[89],"aggregation":[91],"phase,":[92],"thus":[93],"bolstering":[94],"global":[96,120],"model's":[97,121],"perception":[99],"capabilities;":[100],"(2)":[101],"Convex":[102],"Semantic":[103],"Calibration":[104],"(CSC)":[105],"leverages":[106],"convex":[107],"function":[108],"theory":[109],"average":[111],"semantic":[112],"features":[113],"instead":[114],"model":[116],"parameters,":[117],"enhancing":[118],"precision.":[125],"We":[126],"demonstrate":[127,166],"experimentally":[128],"theoretically":[130],"effectiveness":[132],"proposed":[135],"modules":[137],"respectively.":[138],"method":[140],"consistently":[141],"outperforming":[142],"state-of-the-art":[144],"methods":[145],"across":[146],"multiple":[147],"valuable":[148],"application":[149],"scenarios":[150],"2.26%":[152],"7.61%.":[154],"Moreover,":[155],"build":[157],"real":[159],"FL":[160],"system":[161],"using":[162],"Raspberry":[163],"Pis":[164],"that":[167],"our":[168],"approach":[169],"achieves":[170],"good":[172],"trade-off":[173],"between":[174],"performance":[175],"efficiency.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}