{"id":"https://openalex.org/W4403780050","doi":"https://doi.org/10.48550/arxiv.2409.13645","title":"DP$^2$-FedSAM: Enhancing Differentially Private Federated Learning\n Through Personalized Sharpness-Aware Minimization","display_name":"DP$^2$-FedSAM: Enhancing Differentially Private Federated Learning\n Through Personalized Sharpness-Aware Minimization","publication_year":2024,"publication_date":"2024-09-20","ids":{"openalex":"https://openalex.org/W4403780050","doi":"https://doi.org/10.48550/arxiv.2409.13645"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.13645","pdf_url":"http://arxiv.org/pdf/2409.13645","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://arxiv.org/pdf/2409.13645","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076024677","display_name":"Zhenxiao Zhang","orcid":"https://orcid.org/0000-0002-5914-5525"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhenxiao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081180851","display_name":"Yuanxiong Guo","orcid":"https://orcid.org/0000-0003-2241-125X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Yuanxiong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5035287134","display_name":"Yanmin Gong","orcid":"https://orcid.org/0000-0002-1761-2834"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gong, Yanmin","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"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":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":77},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9997,"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":0.9997,"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/T10237","display_name":"Cryptography and Data Security","score":0.9987,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9884,"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/minification","display_name":"Minification","score":0.6366278},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated Learning","score":0.6097874}],"concepts":[{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.6366278},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6097874},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5322924},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.35207146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34799898},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.13860816}],"mesh":[],"locations_count":1,"locations":[{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.13645","pdf_url":"http://arxiv.org/pdf/2409.13645","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.13645","pdf_url":"http://arxiv.org/pdf/2409.13645","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391913857","https://openalex.org/W4391375266","https://openalex.org/W4378677776","https://openalex.org/W4298221930","https://openalex.org/W3176937389","https://openalex.org/W2899084033","https://openalex.org/W2777914285","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433"],"abstract_inverted_index":{"Federated":[0,99],"learning":[1,7,40],"(FL)":[2],"is":[3],"a":[4,16,89,132,137],"distributed":[5],"machine":[6],"approach":[8],"that":[9,169],"allows":[10],"multiple":[11],"clients":[12],"to":[13,60,113,177],"collaboratively":[14],"train":[15],"model":[17,32,63,74,85,126],"without":[18,128],"sharing":[19],"their":[20],"raw":[21],"data.":[22],"To":[23,83,151],"prevent":[24],"sensitive":[25],"information":[26],"from":[27],"being":[28],"inferred":[29],"through":[30],"the":[31,61,66,115,142,153,173,178],"updates":[33],"shared":[34,62],"in":[35,52,72,78,183],"FL,":[36],"differentially":[37],"private":[38],"federated":[39],"(DPFL)":[41],"has":[42],"been":[43],"proposed.":[44],"DPFL":[45,68,91,180],"ensures":[46],"formal":[47],"and":[48,56,97,109,121,144],"rigorous":[49,138],"privacy":[50,143],"protection":[51],"FL":[53],"by":[54],"clipping":[55],"adding":[57],"random":[58],"noise":[59,119],"updates.":[64],"However,":[65],"existing":[67,179],"methods":[69],"often":[70],"result":[71],"severe":[73],"utility":[75,127],"degradation,":[76],"especially":[77],"settings":[79],"with":[80,101],"data":[81,185],"heterogeneity.":[82],"enhance":[84],"utility,":[86],"we":[87,135,157],"propose":[88],"novel":[90],"method":[92,171],"named":[93],"DP$^2$-FedSAM:":[94],"Differentially":[95],"Private":[96],"Personalized":[98],"Learning":[100],"Sharpness-Aware":[102],"Minimization.":[103],"DP$^2$-FedSAM":[104],"leverages":[105],"personalized":[106],"partial":[107],"model-sharing":[108],"sharpness-aware":[110],"minimization":[111],"optimizer":[112],"mitigate":[114],"adverse":[116],"impact":[117],"of":[118,141,147,155],"addition":[120],"clipping,":[122],"thereby":[123],"significantly":[124],"improving":[125],"sacrificing":[129],"privacy.":[130],"From":[131],"theoretical":[133,139],"perspective,":[134],"provide":[136],"analysis":[140],"convergence":[145],"guarantees":[146],"our":[148,170],"proposed":[149],"method.":[150],"evaluate":[152],"effectiveness":[154],"DP$^2$-FedSAM,":[156],"conduct":[158],"extensive":[159],"evaluations":[160],"based":[161],"on":[162],"common":[163],"benchmark":[164],"datasets.":[165],"Our":[166],"results":[167],"verify":[168],"improves":[172],"privacy-utility":[174],"trade-off":[175],"compared":[176],"methods,":[181],"particularly":[182],"heterogeneous":[184],"settings.":[186]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4403780050","counts_by_year":[],"updated_date":"2025-04-16T03:58:22.202273","created_date":"2024-10-26"}