{"id":"https://openalex.org/W7160840242","doi":"https://doi.org/10.48550/arxiv.2605.07860","title":"On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems","display_name":"On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160840242","doi":"https://doi.org/10.48550/arxiv.2605.07860"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.07860","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07860","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.07860","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5106011071","display_name":"Usevalad Milasheuski","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Milasheuski, Usevalad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135843646","display_name":"Piero Baraldi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baraldi, Piero","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135907045","display_name":"Enrico Zio","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zio, Enrico","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5007923049","display_name":"Stefano Savazzi","orcid":"https://orcid.org/0000-0002-9865-6512"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Savazzi, Stefano","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.18870000541210175,"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":0.18870000541210175,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.09290000051259995,"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/T12127","display_name":"Software System Performance and Reliability","score":0.06369999796152115,"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/discriminative-model","display_name":"Discriminative model","score":0.7597000002861023},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6636000275611877},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5401999950408936},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5224000215530396},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.49630001187324524},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.460999995470047},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.39010000228881836}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7667999863624573},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7597000002861023},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6636000275611877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5455999970436096},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5401999950408936},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.522599995136261},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5224000215530396},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.49630001187324524},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.460999995470047},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.39010000228881836},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.38109999895095825},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3693000078201294},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32260000705718994},{"id":"https://openalex.org/C2779965156","wikidata":"https://www.wikidata.org/wiki/Q5227350","display_name":"Data sharing","level":3,"score":0.3107999861240387},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.26109999418258667},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.07860","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07860","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.07860","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07860","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Federated":[0],"Learning":[1],"(FL)":[2],"has":[3],"emerged":[4],"as":[5,38,133],"a":[6,78,121,134,143],"promising":[7],"paradigm":[8],"for":[9,54,124,137],"preserving":[10],"client":[11],"data":[12],"ownership":[13],"and":[14,46,84,96,102,155,160,188],"control":[15],"over":[16,142],"distributed":[17],"Internet":[18],"of":[19,81,89,109],"Things":[20],"(IoT)":[21],"environments.":[22],"While":[23],"discriminative":[24],"models":[25,35,127],"dominate":[26],"most":[27],"FL":[28,162],"use":[29],"cases,":[30],"recent":[31],"advances":[32],"in":[33,58,65,69,86,151,158,205],"generative":[34,126],"--":[36,50,196,200],"such":[37],"Variational":[39],"Autoencoders":[40],"(VAE),":[41],"Generative":[42],"Adversarial":[43],"Networks":[44],"(GAN),":[45],"Diffusion":[47],"Models":[48],"(DM)":[49],"offer":[51],"new":[52],"opportunities":[53],"unsupervised":[55],"anomaly":[56],"detection":[57],"time":[59,145],"series":[60,146],"analysis,":[61,117],"with":[62],"relevant":[63],"applications":[64],"predictive":[66],"maintenance":[67],"(PdM)":[68],"critical":[70],"industrial":[71],"infrastructures.":[72],"In":[73],"this":[74,116],"work,":[75],"we":[76],"present":[77],"comprehensive":[79],"analysis":[80],"VAEs,":[82],"GANs,":[83],"DMs":[85],"the":[87,118,165,180,186],"context":[88],"federated":[90,125],"PdM.":[91],"We":[92],"analyze":[93],"their":[94],"performance":[95],"communication":[97],"overhead":[98],"under":[99],"both":[100],"full":[101,169,203],"partial":[103,130,194],"federation":[104,170,195,204],"setups,":[105],"where":[106],"only":[107],"subsets":[108],"model":[110,138,152,181],"components":[111],"are":[112],"shared.":[113],"Building":[114],"on":[115],"paper":[119],"proposes":[120],"novel":[122],"taxonomy":[123],"that":[128],"formalizes":[129],"component":[131],"sharing":[132,199],"principled":[135],"mechanism":[136],"personalization.":[139],"Our":[140],"experiments":[141],"real-world":[144],"dataset":[147],"reveal":[148],"distinct":[149],"trade-offs":[150],"utility,":[153],"stability,":[154],"scalability,":[156],"especially":[157,197],"heterogeneous":[159],"bandwidth-constrained":[161],"settings.":[163,208],"For":[164,191],"evaluated":[166],"GAN-based":[167],"configurations,":[168],"improves":[171],"training":[172],"stability":[173],"relative":[174],"to":[175],"independent":[176],"local":[177],"training,":[178],"although":[179],"remains":[182],"less":[183],"robust":[184],"than":[185],"VAE-":[187],"DDPM-based":[189],"alternatives.":[190],"DMs,":[192],"however,":[193],"decoder":[198],"can":[201],"outperform":[202],"bandwidth-constrained,":[206],"non-IID":[207]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-12T00:00:00"}