{"id":"https://openalex.org/W7160908895","doi":"https://doi.org/10.48550/arxiv.2605.09144","title":"FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learning","display_name":"FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learning","publication_year":2026,"publication_date":"2026-05-09","ids":{"openalex":"https://openalex.org/W7160908895","doi":"https://doi.org/10.48550/arxiv.2605.09144"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.09144","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09144","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.2605.09144","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101948771","display_name":"Bingnan Xiao","orcid":"https://orcid.org/0009-0007-5360-2856"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Bingnan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135912190","display_name":"Yuan Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057208641","display_name":"Bingcong Li","orcid":"https://orcid.org/0000-0003-1958-4168"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Bingcong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135916613","display_name":"Wei Ni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ni, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135984667","display_name":"Xin Eric Wang","orcid":"https://orcid.org/0000-0003-3001-4676"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135939371","display_name":"Tony Q. S. Quek","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quek, Tony Q. S.","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":0.808899998664856,"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":0.808899998664856,"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":0.0430000014603138,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.03869999945163727,"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/flatness","display_name":"Flatness (cosmology)","score":0.8382999897003174},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.5194000005722046},{"id":"https://openalex.org/keywords/perturbation","display_name":"Perturbation (astronomy)","score":0.439300000667572},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.4350999891757965},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.39899998903274536},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.38199999928474426}],"concepts":[{"id":"https://openalex.org/C2778530986","wikidata":"https://www.wikidata.org/wiki/Q5457948","display_name":"Flatness (cosmology)","level":3,"score":0.8382999897003174},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.573199987411499},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.5194000005722046},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.439300000667572},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.4350999891757965},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.38199999928474426},{"id":"https://openalex.org/C117765406","wikidata":"https://www.wikidata.org/wiki/Q5362437","display_name":"Generalization error","level":3,"score":0.34790000319480896},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3465999960899353},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34380000829696655},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31520000100135803},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C41065033","wikidata":"https://www.wikidata.org/wiki/Q2825412","display_name":"Adversary","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C117898588","wikidata":"https://www.wikidata.org/wiki/Q6664310","display_name":"Local convergence","level":3,"score":0.28850001096725464},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.26669999957084656},{"id":"https://openalex.org/C164752517","wikidata":"https://www.wikidata.org/wiki/Q5570875","display_name":"Global optimization","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C2779188883","wikidata":"https://www.wikidata.org/wiki/Q82446","display_name":"Global Map","level":3,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.09144","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09144","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.2605.09144","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09144","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Sharpness-aware":[0],"minimization":[1,108],"(SAM)":[2],"is":[3,87,171],"an":[4,146],"effective":[5],"method":[6],"for":[7,29,68],"improving":[8,57],"the":[9,37,42,69,82,160,164,168,183],"generalization":[10,66],"of":[11,143,155],"federated":[12],"learning":[13],"(FL)":[14],"by":[15,41,90],"steering":[16],"local":[17,58,91,121,124,148],"training":[18,64],"toward":[19],"flat":[20,31,38],"minima.":[21],"Under":[22],"data":[23,79],"heterogeneity,":[24],"however,":[25],"device-side":[26],"SAM":[27],"searches":[28],"locally":[30],"basins":[32],"that":[33,74,159,176],"are":[34],"incompatible":[35],"with":[36,105],"region":[39],"preferred":[40],"global":[43,70,127,140,169],"objective.":[44],"We":[45,72,150],"identify":[46],"this":[47,99],"structural":[48],"failure":[49],"mode":[50],"as":[51],"flatness":[52,59,75,122,179],"incompatibility,":[53],"which":[54,110],"explains":[55],"why":[56],"alone":[60],"may":[61],"provide":[62],"limited":[63],"and":[65,81,86,93,116,126,133,157,167,181],"improvement":[67],"model.":[71],"reveal":[73],"incompatibility":[76,180],"arises":[77],"from":[78],"heterogeneity":[80],"friendly":[83],"adversary":[84],"phenomenon,":[85],"further":[88],"amplified":[89],"updates":[92],"partial":[94],"device":[95],"participation.":[96],"To":[97],"mitigate":[98],"issue,":[100],"we":[101],"propose":[102],"Federated":[103],"Learning":[104],"variance-suppressed":[106,113],"sharpness-aware":[107],"(FedVSSAM),":[109],"constructs":[111],"a":[112,137],"adjusted":[114,165],"direction":[115,166],"uses":[117],"it":[118],"consistently":[119],"in":[120],"search,":[123],"descent,":[125],"update.":[128],"FedVSSAM":[129,156,177],"anchors":[130],"both":[131],"perturbation":[132],"update":[134],"directions":[135],"to":[136],"more":[138],"stable":[139],"direction,":[141],"instead":[142],"correcting":[144],"only":[145],"isolated":[147],"perturbation.":[149],"establish":[151],"non-convex":[152],"convergence":[153],"guarantees":[154],"prove":[158],"mean-square":[161],"deviation":[162],"between":[163],"gradient":[170],"effectively":[172],"controlled.":[173],"Experiments":[174],"demonstrate":[175],"mitigates":[178],"outperforms":[182],"baselines":[184],"across":[185],"diverse":[186],"FL":[187],"settings.":[188]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}
