{"id":"https://openalex.org/W4401508790","doi":"https://doi.org/10.1109/meditcom61057.2024.10621155","title":"Compressing and Fine-tuning DNNs for Efficient Inference in Mobile Device-Edge Continuum","display_name":"Compressing and Fine-tuning DNNs for Efficient Inference in Mobile Device-Edge Continuum","publication_year":2024,"publication_date":"2024-07-08","ids":{"openalex":"https://openalex.org/W4401508790","doi":"https://doi.org/10.1109/meditcom61057.2024.10621155"},"language":"en","primary_location":{"id":"doi:10.1109/meditcom61057.2024.10621155","is_oa":false,"landing_page_url":"https://doi.org/10.1109/meditcom61057.2024.10621155","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom)","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/A5077790654","display_name":"Gurtaj Singh","orcid":"https://orcid.org/0009-0009-7170-0745"},"institutions":[{"id":"https://openalex.org/I59725666","display_name":"University of Reggio Calabria","ror":"https://ror.org/041sz8d87","country_code":"IT","type":"education","lineage":["https://openalex.org/I59725666"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Gurtaj Singh","raw_affiliation_strings":["University Mediterranea of Reggio,Calabria,Italy"],"affiliations":[{"raw_affiliation_string":"University Mediterranea of Reggio,Calabria,Italy","institution_ids":["https://openalex.org/I59725666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039918126","display_name":"Olga Chukhno","orcid":"https://orcid.org/0000-0003-4271-5744"},"institutions":[{"id":"https://openalex.org/I59725666","display_name":"University of Reggio Calabria","ror":"https://ror.org/041sz8d87","country_code":"IT","type":"education","lineage":["https://openalex.org/I59725666"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Olga Chukhno","raw_affiliation_strings":["University Mediterranea of Reggio,Calabria,Italy"],"affiliations":[{"raw_affiliation_string":"University Mediterranea of Reggio,Calabria,Italy","institution_ids":["https://openalex.org/I59725666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012514472","display_name":"Claudia Campolo","orcid":"https://orcid.org/0000-0003-3281-6680"},"institutions":[{"id":"https://openalex.org/I59725666","display_name":"University of Reggio Calabria","ror":"https://ror.org/041sz8d87","country_code":"IT","type":"education","lineage":["https://openalex.org/I59725666"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Claudia Campolo","raw_affiliation_strings":["University Mediterranea of Reggio,Calabria,Italy"],"affiliations":[{"raw_affiliation_string":"University Mediterranea of Reggio,Calabria,Italy","institution_ids":["https://openalex.org/I59725666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026273554","display_name":"Antonella Molinaro","orcid":"https://orcid.org/0000-0003-2731-300X"},"institutions":[{"id":"https://openalex.org/I59725666","display_name":"University of Reggio Calabria","ror":"https://ror.org/041sz8d87","country_code":"IT","type":"education","lineage":["https://openalex.org/I59725666"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Antonella Molinaro","raw_affiliation_strings":["University Mediterranea of Reggio,Calabria,Italy"],"affiliations":[{"raw_affiliation_string":"University Mediterranea of Reggio,Calabria,Italy","institution_ids":["https://openalex.org/I59725666"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021780258","display_name":"Carla Fabiana Chiasserini","orcid":"https://orcid.org/0000-0003-1410-660X"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Polytechnic University of Turin","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Carla Fabiana Chiasserini","raw_affiliation_strings":["Politecnico di Torino,Italy"],"affiliations":[{"raw_affiliation_string":"Politecnico di Torino,Italy","institution_ids":["https://openalex.org/I177477856"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5077790654"],"corresponding_institution_ids":["https://openalex.org/I59725666"],"apc_list":null,"apc_paid":null,"fwci":0.8249,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.72738793,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"305","last_page":"310"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9973999857902527,"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":0.9973999857902527,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9930999875068665,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6629956960678101},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6317285299301147},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5239620208740234},{"id":"https://openalex.org/keywords/fine-tuning","display_name":"Fine-tuning","score":0.4925099313259125},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.4748953878879547},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.21287736296653748},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1832817792892456},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.04959675669670105}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6629956960678101},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6317285299301147},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5239620208740234},{"id":"https://openalex.org/C157524613","wikidata":"https://www.wikidata.org/wiki/Q2828883","display_name":"Fine-tuning","level":2,"score":0.4925099313259125},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.4748953878879547},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.21287736296653748},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1832817792892456},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.04959675669670105},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/meditcom61057.2024.10621155","is_oa":false,"landing_page_url":"https://doi.org/10.1109/meditcom61057.2024.10621155","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334322","display_name":"HORIZON EUROPE Framework Programme","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2764043458","https://openalex.org/W2767101511","https://openalex.org/W2902991518","https://openalex.org/W2915589364","https://openalex.org/W2965862774","https://openalex.org/W2977090839","https://openalex.org/W3013610879","https://openalex.org/W3087194612","https://openalex.org/W3114473259","https://openalex.org/W3159788821","https://openalex.org/W3184606595","https://openalex.org/W4297775537","https://openalex.org/W4309570708","https://openalex.org/W4378473300","https://openalex.org/W4385490892","https://openalex.org/W4386262113","https://openalex.org/W4386987491","https://openalex.org/W6737664043","https://openalex.org/W6745148473","https://openalex.org/W6756826759","https://openalex.org/W6759263581","https://openalex.org/W6775385080","https://openalex.org/W6783500014","https://openalex.org/W6794265893"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W2116305750"],"abstract_inverted_index":{"Pruning":[0],"deep":[1],"neural":[2],"networks":[3],"(DNN)":[4],"is":[5,32,56],"a":[6,12,49,85,95,184,188,194],"well-known":[7],"technique":[8],"that":[9,156],"allows":[10],"for":[11,81,115,122],"sensible":[13],"reduction":[14,190],"in":[15,37,40,51,136],"inference":[16,99,146,167],"cost.":[17],"However,":[18],"this":[19,74],"may":[20],"severely":[21],"degrade":[22],"the":[23,27,30,60,78,82,110,127,144,157,159,164,169,176,180,200,204],"accuracy":[24,64,149],"achieved":[25],"by":[26],"model":[28,66],"unless":[29],"latter":[31],"properly":[33],"fine-tuned,":[34],"which":[35],"may,":[36],"turn,":[38],"result":[39],"increased":[41],"computational":[42],"cost":[43,189],"and":[44,68,70,92,101,105,133,140,148,163],"latency.":[45],"Thus,":[46],"upon":[47],"deploying":[48,199],"DNN":[50],"resource-constrained":[52],"edge":[53,103,132,160],"environments,":[54],"it":[55,172],"critical":[57],"to":[58,112,174,193,198],"find":[59],"best":[61],"trade-off":[62],"between":[63],"(hence,":[65],"complexity)":[67],"latency":[69,147],"energy":[71,139],"consumption.":[72],"In":[73,151],"work,":[75],"we":[76],"explore":[77],"different":[79],"options":[80],"deployment":[83],"of":[84,126,138,166,191],"machine":[86],"learning":[87],"pipeline,":[88],"encompassing":[89],"pruning,":[90],"finetuning,":[91],"inference,":[93],"across":[94,130],"mobile":[96,134,181],"device":[97,135,182],"requesting":[98],"tasks":[100,129],"an":[102,123],"server,":[104],"considering":[106],"privacy":[107],"constraints":[108],"on":[109],"data":[111],"be":[113],"used":[114],"fine-tuning.":[116],"Our":[117],"experimental":[118],"analysis":[119],"provides":[120],"insights":[121],"efficient":[124],"allocation":[125],"pipeline":[128,178,202],"network":[131,141],"terms":[137],"costs,":[142],"as":[143],"target":[145],"vary.":[150],"particular,":[152],"our":[153],"results":[154],"highlight":[155],"higher":[158],"server":[161],"load":[162],"number":[165],"requests,":[168],"more":[170],"convenient":[171],"becomes":[173],"deploy":[175],"entire":[177],"at":[179,203],"using":[183],"pruned":[185],"model,":[186],"with":[187],"up":[192],"factor":[195],"two":[196],"compared":[197],"whole":[201],"edge.":[205]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}
