{"id":"https://openalex.org/W7134841882","doi":"https://doi.org/10.48550/arxiv.2603.06741","title":"Heterogeneous Decentralized Diffusion Models","display_name":"Heterogeneous Decentralized Diffusion Models","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7134841882","doi":"https://doi.org/10.48550/arxiv.2603.06741"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.06741","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128656367","display_name":"Zhiying Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jiang, Zhiying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128652006","display_name":"Raihan Seraj","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seraj, Raihan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076869132","display_name":"Marcos Villagra","orcid":"https://orcid.org/0000-0002-6081-9099"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Villagra, Marcos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128642124","display_name":"Bidhan Roy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roy, Bidhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5128656367"],"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9370999932289124,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9370999932289124,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.00570000009611249,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.004999999888241291,"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/initialization","display_name":"Initialization","score":0.6783999800682068},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5200999975204468},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.49410000443458557},{"id":"https://openalex.org/keywords/synchronization","display_name":"Synchronization (alternating current)","score":0.4823000133037567},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4706000089645386},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.41040000319480896},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4056999981403351},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.3961000144481659},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.38280001282691956}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.745199978351593},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.6783999800682068},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5200999975204468},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.49410000443458557},{"id":"https://openalex.org/C2778562939","wikidata":"https://www.wikidata.org/wiki/Q1298791","display_name":"Synchronization (alternating current)","level":3,"score":0.4823000133037567},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4706000089645386},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.42669999599456787},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.41040000319480896},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4056999981403351},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3961000144481659},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.38280001282691956},{"id":"https://openalex.org/C172430144","wikidata":"https://www.wikidata.org/wiki/Q17111997","display_name":"Symmetric multiprocessor system","level":2,"score":0.3752000033855438},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.37369999289512634},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.35249999165534973},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3179999887943268},{"id":"https://openalex.org/C3913047","wikidata":"https://www.wikidata.org/wiki/Q1956265","display_name":"sync","level":3,"score":0.3167000114917755},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.3098999857902527},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.27059999108314514},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.25850000977516174}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.06741","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.06741","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.06741","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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.06741","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5872176289558411,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Training":[0],"frontier-scale":[1],"diffusion":[2],"models":[3],"often":[4],"requires":[5],"substantial":[6],"computational":[7],"resources":[8],"concentrated":[9],"in":[10,28],"tightly":[11],"coupled":[12],"clusters,":[13],"limiting":[14],"participation":[15],"to":[16,70,100,129,144,152],"well-resourced":[17],"institutions.":[18],"While":[19],"Decentralized":[20],"Diffusion":[21],"Models":[22],"(DDM)":[23],"enable":[24],"training":[25,37,54,65,131],"multiple":[26],"experts":[27,69],"isolation,":[29],"existing":[30],"approaches":[31],"require":[32],"1176":[33,143],"GPU-days":[34,146],"and":[35,75,106,112,148,169,186],"homogeneous":[36,179],"objectives":[38,73],"across":[39],"all":[40],"experts.":[41],"We":[42],"present":[43],"an":[44],"efficient":[45,115],"framework":[46,192],"that":[47,67],"reduces":[48,140],"resource":[49],"requirements":[50,185,195],"while":[51,120],"supporting":[52],"heterogeneous":[53,63,160],"objectives.":[55],"Our":[56],"approach":[57,139],"combines":[58],"three":[59],"contributions:":[60],"(1)":[61],"a":[62,83,88],"decentralized":[64,197],"paradigm":[66],"allows":[68],"use":[71],"different":[72],"(DDPM":[74],"Flow":[76,101],"Matching),":[77],"unified":[78],"at":[79],"inference":[80,157],"time":[81],"via":[82],"deterministic":[84],"schedule-aware":[85],"conversion":[86,97],"into":[87],"common":[89],"velocity":[90],"space":[91],"without":[92,109],"retraining;":[93],"(2)":[94],"pretrained":[95],"checkpoint":[96],"from":[98,142,150],"ImageNet-DDPM":[99],"Matching":[102],"objectives,":[103,190],"accelerating":[104],"convergence":[105],"enabling":[107,187],"initialization":[108],"objective-specific":[110],"pretraining;":[111],"(3)":[113],"PixArt-alpha's":[114],"AdaLN-Single":[116],"architecture,":[117],"reducing":[118],"parameters":[119],"maintaining":[121],"quality.":[122],"Experiments":[123],"on":[124],"LAION-Aesthetics":[125],"show":[126],"that,":[127],"relative":[128],"the":[130,178],"scale":[132],"reported":[133],"for":[134,196],"prior":[135],"DDM":[136],"work,":[137],"our":[138,159,191],"compute":[141],"72":[145],"(16x)":[147],"data":[149],"158M":[151],"11M":[153],"(14x).":[154],"Under":[155],"aligned":[156],"settings,":[158],"2DDPM:6FM":[161],"configuration":[162],"achieves":[163],"better":[164],"FID":[165],"(11.88":[166],"vs.":[167,175],"12.45)":[168],"higher":[170],"intra-prompt":[171],"diversity":[172],"(LPIPS":[173],"0.631":[174],"0.617)":[176],"than":[177],"8FM":[180],"baseline.":[181],"By":[182],"eliminating":[183],"synchronization":[184],"mixed":[188],"DDPM/FM":[189],"lowers":[193],"infrastructure":[194],"generative":[198],"model":[199],"training.":[200]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-03-11T00:00:00"}
