{"id":"https://openalex.org/W7161976018","doi":"https://doi.org/10.48550/arxiv.2605.20276","title":"OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization","display_name":"OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization","publication_year":2026,"publication_date":"2026-05-19","ids":{"openalex":"https://openalex.org/W7161976018","doi":"https://doi.org/10.48550/arxiv.2605.20276"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.20276","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20276","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.20276","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002531423","display_name":"Wei-Bin Kou","orcid":"https://orcid.org/0000-0001-9817-791X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kou, Wei-Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136622627","display_name":"Guangxu Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Guangxu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136675931","display_name":"Ming Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136638186","display_name":"Chen Zhang","orcid":"https://orcid.org/0000-0001-8508-8969"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136706124","display_name":"Lisheng Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Lisheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136722623","display_name":"Lei Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5020953714","display_name":"Yujiu Yang","orcid":"https://orcid.org/0000-0002-6427-1024"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Yujiu","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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.3882000148296356,"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"}},"topics":[{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.3882000148296356,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.21289999783039093,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.0851999968290329,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/regularization","display_name":"Regularization (linguistics)","score":0.6722999811172485},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5616000294685364},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.4912000000476837},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.43959999084472656},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.43860000371932983},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4325000047683716},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.4172999858856201}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7404999732971191},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6722999811172485},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5616000294685364},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.4912000000476837},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.43959999084472656},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.43860000371932983},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4325000047683716},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.4172999858856201},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.40230000019073486},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.3961000144481659},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.3910999894142151},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.3853999972343445},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3815999925136566},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.29600000381469727},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2840000092983246},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.2587999999523163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.20276","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20276","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.20276","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20276","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":"Preprint"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.6863251328468323}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"global":[1,41],"deployment":[2],"of":[3,64],"edge":[4],"intelligence":[5],"operates":[6],"across":[7,245],"heterogeneous":[8],"legal":[9],"frameworks.":[10],"While":[11],"some":[12],"regions":[13],"permit":[14],"centralized":[15,229],"learning":[16,29],"(CL)":[17],"via":[18,92],"cloud":[19],"data":[20,25],"aggregation,":[21],"others":[22],"enforce":[23],"strict":[24,216],"localization,":[26],"necessitating":[27],"federated":[28,175,231],"(FL).":[30],"This":[31],"operational":[32],"dichotomy":[33],"introduces":[34],"two":[35,66],"incompatible":[36],"optimization":[37],"regimes":[38],"(i.e.,":[39],"unbiased":[40],"gradients":[42],"yet":[43],"coupled":[44],"with":[45],"internal":[46,118],"covariate":[47,119],"shift":[48],"in":[49,56,59,120,125,227],"CL":[50,121,190],"versus":[51],"biased,":[52],"drift-prone":[53],"local":[54],"updates":[55,193],"FL),":[57],"resulting":[58],"that":[60,81,162,177,187,203,205,221],"any":[61],"naive":[62],"integration":[63],"the":[65,148,164,179,234],"lacks":[67],"rigorous":[68],"theoretical":[69],"guarantees.":[70],"To":[71],"fill":[72],"this":[73],"gap,":[74],"we":[75,105,151],"propose":[76,106],"OmniISR,":[77],"a":[78,154,174,184],"unified":[79],"framework":[80],"fuses":[82],"pure":[83,85],"CL,":[84],"FL,":[86,126],"and":[87,96,122,127,143,157,191,197,212,230,239],"hybrid":[88,207],"CL-FL":[89,206,235],"training":[90],"modes":[91],"equipping":[93],"intermediate":[94,113,134],"supervision":[95,114],"regularization":[97],"(ISR)":[98],"signals":[99],"at":[100],"multiple":[101,246],"hidden":[102],"layers.":[103],"Specifically,":[104],"(i)":[107,153],"to":[108,115,129,136],"use":[109],"mutual-information":[110],"(MI)":[111],"as":[112,133],"align":[116],"shifting":[117],"client-drifting":[123],"representations":[124],"(ii)":[128,173],"adopt":[130],"negative-entropy":[131],"(NE)":[132],"regularizer":[135],"penalize":[137],"overconfident":[138],"prediction,":[139],"preserve":[140],"representational":[141],"uncertainty,":[142],"avoid":[144],"device-specific":[145],"collapse.":[146],"On":[147],"theory":[149],"side,":[150],"derive":[152],"unified,":[155],"ISR-agnostic,":[156],"non-asymptotic":[158],"O(1/sqrt(T))":[159],"convergence":[160],"bound":[161,202],"shows":[163],"introduced":[165],"ISR":[166],"does":[167],"not":[168],"violate":[169],"standard":[170],"SGD":[171],"convergence,":[172],"drift-bound":[176],"quantifies":[178],"ISR-reduced":[180],"client":[181],"drift,":[182],"(iii)":[183],"gradient-alignment":[185],"guarantee":[186],"ensures":[188],"non-conflicting":[189],"FL":[192,247],"under":[194],"mild":[195],"bias,":[196],"(iv)":[198],"an":[199],"explicit":[200],"escape-time":[201],"indicates":[204],"mixing":[208],"enlarges":[209],"effective":[210],"stochasticity":[211],"accelerates":[213],"escape":[214],"from":[215],"saddles.":[217],"Extensive":[218],"experiments":[219],"demonstrate":[220],"OmniISR":[222],"consistently":[223],"improves":[224],"model":[225],"performance":[226],"both":[228],"paradigms,":[232],"reduces":[233],"gap":[236],"by":[237],"22.60%,":[238],"yields":[240],"37/48":[241],"paired":[242],"metric":[243],"wins":[244],"algorithms.":[248]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-22T00:00:00"}
