{"id":"https://openalex.org/W4405014153","doi":"https://doi.org/10.1145/3636534.3690705","title":"LATTE: Layer Algorithm-aware Training Time Estimation for Heterogeneous Federated Learning","display_name":"LATTE: Layer Algorithm-aware Training Time Estimation for Heterogeneous Federated Learning","publication_year":2024,"publication_date":"2024-12-04","ids":{"openalex":"https://openalex.org/W4405014153","doi":"https://doi.org/10.1145/3636534.3690705"},"language":"en","primary_location":{"id":"doi:10.1145/3636534.3690705","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3636534.3690705","pdf_url":null,"source":null,"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th Annual International Conference on Mobile Computing and Networking","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/A5102990096","display_name":"Kun Wang","orcid":"https://orcid.org/0000-0002-0149-9857"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Kun Wang","raw_affiliation_strings":["City University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011140675","display_name":"Zimu Zhou","orcid":"https://orcid.org/0000-0002-5457-6967"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zimu Zhou","raw_affiliation_strings":["City University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100419083","display_name":"Zhenjiang Li","orcid":"https://orcid.org/0000-0002-3296-3392"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zhenjiang Li","raw_affiliation_strings":["City University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102990096"],"corresponding_institution_ids":["https://openalex.org/I168719708"],"apc_list":null,"apc_paid":null,"fwci":2.7254,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.91738656,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1470","last_page":"1484"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9995999932289124,"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":0.9995999932289124,"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/T13553","display_name":"Age of Information Optimization","score":0.9758999943733215,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9757000207901001,"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/computer-science","display_name":"Computer science","score":0.8222358226776123},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6640452146530151},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.6206775307655334},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.5403552055358887},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.43359825015068054},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4053117334842682},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3985239267349243},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.3601289987564087},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3581327199935913}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8222358226776123},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6640452146530151},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.6206775307655334},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5403552055358887},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.43359825015068054},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4053117334842682},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3985239267349243},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3601289987564087},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3581327199935913},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3636534.3690705","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3636534.3690705","pdf_url":null,"source":null,"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th Annual International Conference on Mobile Computing and Networking","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1487205592","display_name":null,"funder_award_id":"11202623","funder_id":"https://openalex.org/F4320321592","funder_display_name":"Research Grants Council, University Grants Committee"},{"id":"https://openalex.org/G4569554029","display_name":null,"funder_award_id":"9610633","funder_id":"https://openalex.org/F4320309893","funder_display_name":"City University of Hong Kong"},{"id":"https://openalex.org/G6144890231","display_name":null,"funder_award_id":"11205624","funder_id":"https://openalex.org/F4320321592","funder_display_name":"Research Grants Council, University Grants Committee"}],"funders":[{"id":"https://openalex.org/F4320309893","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23"},{"id":"https://openalex.org/F4320321592","display_name":"Research Grants Council, University Grants Committee","ror":"https://ror.org/00djwmt25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W134960717","https://openalex.org/W2194775991","https://openalex.org/W2541884796","https://openalex.org/W2612690371","https://openalex.org/W2618851150","https://openalex.org/W2766975872","https://openalex.org/W2796438033","https://openalex.org/W2798720628","https://openalex.org/W2804032941","https://openalex.org/W2884700152","https://openalex.org/W2886851211","https://openalex.org/W2903557836","https://openalex.org/W2912213068","https://openalex.org/W2982676829","https://openalex.org/W3000666106","https://openalex.org/W3088731039","https://openalex.org/W3090393599","https://openalex.org/W3103802018","https://openalex.org/W3105122387","https://openalex.org/W3106416029","https://openalex.org/W3133814152","https://openalex.org/W3134509799","https://openalex.org/W3144271226","https://openalex.org/W3165447760","https://openalex.org/W3169198323","https://openalex.org/W3196371845","https://openalex.org/W3198130589","https://openalex.org/W3203503583","https://openalex.org/W3210103168","https://openalex.org/W3211351785","https://openalex.org/W3213321731","https://openalex.org/W4212774754","https://openalex.org/W4221156340","https://openalex.org/W4282960063","https://openalex.org/W4283796783","https://openalex.org/W4285105424","https://openalex.org/W4288083516","https://openalex.org/W4290059224","https://openalex.org/W4306178486","https://openalex.org/W4306178637","https://openalex.org/W4310822417","https://openalex.org/W4317927968","https://openalex.org/W4376149513","https://openalex.org/W4380925616","https://openalex.org/W4385568042","https://openalex.org/W4385800841","https://openalex.org/W4387092277","https://openalex.org/W4387686998","https://openalex.org/W4389476196","https://openalex.org/W4390968835","https://openalex.org/W4399347590","https://openalex.org/W6713134421","https://openalex.org/W6744307745","https://openalex.org/W6752515464","https://openalex.org/W6759238902","https://openalex.org/W6862200595"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W3216976533","https://openalex.org/W100620283","https://openalex.org/W2495260952","https://openalex.org/W4394050964","https://openalex.org/W2551249631"],"abstract_inverted_index":{"Accurate":[0],"estimation":[1],"of":[2,35,43,55,83,86,97,126,154,172,182,193],"on-device":[3],"model":[4,28,99,155,244],"training":[5,132,215,221,240],"time":[6,153,167,222],"is":[7,119],"increasingly":[8],"required":[9],"for":[10,49,113],"emerging":[11],"learning":[12,22,88,107,233],"paradigms":[13],"on":[14,61,123,205],"mobile":[15,36],"edge":[16,37],"devices,":[17],"such":[18],"as":[19,227],"heterogeneous":[20],"federated":[21,183],"(HFL).":[23],"HFL":[24,72],"usually":[25],"customizes":[26],"the":[27,32,80,115,120,163,170,173,179,199],"architecture":[29],"according":[30],"to":[31,39,53,65,145,149,161,168,247],"different":[33,102,111,131,231],"capabilities":[34],"devices":[38,48],"ensure":[40,150],"efficient":[41],"use":[42],"local":[44],"data":[45],"from":[46,137],"all":[47],"training.":[50,184],"However,":[51],"due":[52],"oversimplification":[54],"latency":[56,171,216],"modeling,":[57],"existing":[58,77,138],"methods":[59,78],"rely":[60],"a":[62,98,124,194],"single":[63],"coefficient":[64],"represent":[66],"computational":[67],"heterogeneity,":[68],"resulting":[69,129],"in":[70,130,143],"sub-optimal":[71],"efficiency.":[73],"We":[74,224],"find":[75],"that":[76,197],"ignore":[79],"important":[81],"impact":[82],"runtime":[84,127,203,207],"optimization":[85],"deep":[87,106,232],"frameworks,":[89],"which":[90,191],"we":[91,157,188],"call":[92],"development-chain":[93],"diversity.":[94],"Specifically,":[95],"layers":[96],"may":[100],"have":[101,110],"algorithm":[103,116,165,201],"implementations,":[104],"and":[105,134,243],"frameworks":[108],"often":[109],"strategies":[112],"selecting":[114],"they":[117],"believe":[118],"best":[121,164,200],"based":[122,204],"range":[125],"factors,":[128],"latencies":[133],"invalid":[135],"predictions":[136],"methods.":[139,249],"In":[140],"this":[141,147,186],"paper,":[142],"addition":[144],"considering":[146],"diversity":[148],"synchronized":[151],"completion":[152],"training,":[156,175],"also":[158],"study":[159],"how":[160],"select":[162],"each":[166],"reduce":[169],"per-round":[174],"thereby":[176],"further":[177,210],"improving":[178],"overall":[180],"efficiency":[181],"To":[185],"end,":[187],"propose":[189],"LATTE,":[190],"consists":[192],"novel":[195],"selector":[196],"identifies":[198],"at":[202],"relative":[206],"factors.":[208],"By":[209],"integrating":[211],"it":[212],"into":[213],"our":[214],"model,":[217],"LATTE":[218,226],"provides":[219],"accurate":[220],"estimation.":[223],"develop":[225],"middleware,":[228],"compatible":[229],"with":[230],"frameworks.":[234],"Extensive":[235],"results":[236],"show":[237],"significantly":[238],"improved":[239],"convergence":[241],"speed":[242],"accuracy":[245],"compared":[246],"state-of-the-art":[248]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
