{"id":"https://openalex.org/W7140215410","doi":"https://doi.org/10.48550/arxiv.2603.21660","title":"OmniFM: Toward Modality-Robust and Task-Agnostic Federated Learning for Heterogeneous Medical Imaging","display_name":"OmniFM: Toward Modality-Robust and Task-Agnostic Federated Learning for Heterogeneous Medical Imaging","publication_year":2026,"publication_date":"2026-03-23","ids":{"openalex":"https://openalex.org/W7140215410","doi":"https://doi.org/10.48550/arxiv.2603.21660"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.21660","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21660","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":null,"license_id":null,"version":null,"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":"https://doi.org/10.48550/arxiv.2603.21660","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Liu, Meilin","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Liu, Meilin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Jiaying","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiaying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Shan, Jing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shan, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.396699994802475,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.396699994802475,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.10840000212192535,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.08510000258684158,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.7993000149726868},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6229000091552734},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5623999834060669},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.5426999926567078},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.5278000235557556},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.43230000138282776},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.30809998512268066}],"concepts":[{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7993000149726868},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7472000122070312},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6229000091552734},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5623999834060669},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5426999926567078},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.5278000235557556},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.43230000138282776},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3644999861717224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3555999994277954},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.31839999556541443},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.29089999198913574},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.2797999978065491},{"id":"https://openalex.org/C138020889","wikidata":"https://www.wikidata.org/wiki/Q2349659","display_name":"Collaborative learning","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.21660","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21660","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.21660","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21660","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.5236831903457642,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the 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],"become":[4],"a":[5,54,81,132],"promising":[6],"paradigm":[7],"for":[8],"collaborative":[9],"medical":[10],"image":[11],"analysis,":[12],"yet":[13],"existing":[14],"frameworks":[15],"remain":[16],"tightly":[17],"coupled":[18],"to":[19,105,114,122],"task-specific":[20],"backbones":[21],"and":[22,41,56,70,92,117,126,153,162],"are":[23],"fragile":[24],"under":[25,159],"heterogeneous":[26],"imaging":[27],"modalities.":[28],"Such":[29],"constraints":[30],"hinder":[31],"real-world":[32,141],"deployment,":[33],"where":[34],"institutions":[35],"vary":[36],"widely":[37],"in":[38],"modality":[39],"distributions":[40],"must":[42],"support":[43],"diverse":[44],"downstream":[45],"tasks.":[46],"To":[47],"address":[48],"this":[49],"limitation,":[50],"we":[51],"propose":[52],"OmniFM,":[53],"modality-":[55],"task-agnostic":[57],"FL":[58,149],"framework":[59],"that":[60,136,144],"unifies":[61],"training":[62],"across":[63,151],"classification,":[64],"segmentation,":[65],"super-resolution,":[66],"visual":[67],"question":[68],"answering,":[69],"multimodal":[71],"fusion":[72],"without":[73],"re-engineering":[74],"the":[75],"optimization":[76],"pipeline.":[77],"OmniFM":[78,98,145],"builds":[79],"on":[80,140],"key":[82],"frequency-domain":[83],"insight:":[84],"low-frequency":[85],"spectral":[86],"components":[87],"exhibit":[88],"strong":[89],"cross-modality":[90,154],"consistency":[91],"encode":[93],"modality-invariant":[94],"anatomical":[95],"structures.":[96],"Accordingly,":[97],"integrates":[99],"(i)":[100],"Global":[101],"Spectral":[102,120],"Knowledge":[103],"Retrieval":[104],"inject":[106],"global":[107,125],"frequency":[108],"priors,":[109],"(ii)":[110],"Embedding-wise":[111],"Cross-Attention":[112],"Fusion":[113],"align":[115],"representations,":[116],"(iii)":[118],"Prefix-Suffix":[119],"Prompting":[121],"jointly":[123],"condition":[124],"personalized":[127],"cues,":[128],"together":[129],"regularized":[130],"by":[131],"Spectral-Proximal":[133],"Alignment":[134],"objective":[135],"stabilizes":[137],"aggregation.":[138],"Experiments":[139],"datasets":[142],"show":[143],"consistently":[146],"surpasses":[147],"state-of-the-art":[148],"baselines":[150],"intra-":[152],"heterogeneity,":[155],"achieving":[156],"superior":[157],"results":[158],"both":[160],"fine-tuning":[161],"training-from-scratch":[163],"setups.":[164]},"counts_by_year":[],"updated_date":"2026-04-25T08:17:42.794288","created_date":"2026-03-25T00:00:00"}
