{"id":"https://openalex.org/W7159644005","doi":"https://doi.org/10.48550/arxiv.2604.27434","title":"AdaBFL: Multi-Layer Defensive Adaptive Aggregation for Bzantine-Robust Federated Learning","display_name":"AdaBFL: Multi-Layer Defensive Adaptive Aggregation for Bzantine-Robust Federated Learning","publication_year":2026,"publication_date":"2026-04-30","ids":{"openalex":"https://openalex.org/W7159644005","doi":"https://doi.org/10.48550/arxiv.2604.27434"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.27434","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27434","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.2604.27434","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114240090","display_name":"Zehui Tang","orcid":"https://orcid.org/0009-0000-5469-7331"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Zehui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134975680","display_name":"Yuchen Liu","orcid":"https://orcid.org/0009-0008-3636-1779"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yuchen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134948954","display_name":"Feihu Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Feihu","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":0.8871999979019165,"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":0.8871999979019165,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.06040000170469284,"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":0.006899999920278788,"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/federated-learning","display_name":"Federated learning","score":0.9225000143051147},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5756000280380249},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"Distributed learning","score":0.5364999771118164},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.39750000834465027},{"id":"https://openalex.org/keywords/server","display_name":"Server","score":0.3662000000476837},{"id":"https://openalex.org/keywords/adaptive-learning","display_name":"Adaptive learning","score":0.3165999948978424}],"concepts":[{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.9225000143051147},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8235999941825867},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5756000280380249},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.5364999771118164},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43709999322891235},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.39750000834465027},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.37040001153945923},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3702999949455261},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.3662000000476837},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.34119999408721924},{"id":"https://openalex.org/C125014702","wikidata":"https://www.wikidata.org/wiki/Q4680749","display_name":"Adaptive learning","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C99221444","wikidata":"https://www.wikidata.org/wiki/Q1532069","display_name":"Private information retrieval","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.27434","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27434","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.2604.27434","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27434","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":[{"id":"https://metadata.un.org/sdg/16","score":0.6192198395729065,"display_name":"Peace, Justice and strong institutions"}],"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":[1,7,101],"(FL)":[2],"is":[3],"a":[4,24,105],"popular":[5],"distributed":[6],"paradigm":[8],"in":[9,81],"machine":[10],"learning,":[11],"which":[12,110],"enables":[13],"multiple":[14,71,142],"clients":[15,43],"to":[16,38,48,66,119],"collaboratively":[17],"train":[18],"models":[19,47],"under":[20,132],"the":[21,50,79,82,114,133,145,151],"guidance":[22],"of":[23,73,116,128,147],"server":[25],"without":[26],"exposing":[27],"private":[28],"client":[29],"data.":[30,138],"However,":[31],"FL's":[32],"decentralized":[33],"nature":[34],"makes":[35],"it":[36],"vulnerable":[37],"poisoning":[39],"attacks,":[40,55],"where":[41],"malicious":[42],"can":[44,111],"submit":[45],"corrupted":[46],"manipulate":[49],"system.":[51],"To":[52,84],"counter":[53,120],"such":[54],"although":[56],"various":[57],"Byzantine-robust":[58],"methods":[59,64],"have":[60],"been":[61],"proposed,":[62],"these":[63,87],"struggle":[65],"provide":[67,125],"balanced":[68],"defense":[69,117],"against":[70],"types":[72],"attacks":[74],"or":[75],"rely":[76],"on":[77,104,136],"possessing":[78],"dataset":[80],"server.":[83],"deal":[85],"with":[86],"drawbacks,":[88],"thus,":[89],"we":[90,124],"propose":[91],"an":[92],"effective":[93],"multi-layer":[94],"defensive":[95,108],"adaptive":[96],"aggregation":[97],"for":[98],"Bzantine-robust":[99],"federated":[100],"(AdaBFL)":[102],"based":[103],"novel":[106],"three-layer":[107],"mechanism,":[109],"adaptively":[112],"adjust":[113],"weights":[115],"algorithms":[118],"complex":[121],"attacks.":[122],"Moreover,":[123],"convergence":[126],"properties":[127],"our":[129,148],"AdaBFL":[130,149],"method":[131],"non-convex":[134],"setting":[135],"non-iid":[137],"Comprehensive":[139],"experiments":[140],"across":[141],"datasets":[143],"validate":[144],"superiority":[146],"over":[150],"comparable":[152],"algorithms.":[153]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-02T00:00:00"}
