{"id":"https://openalex.org/W7148115669","doi":"https://doi.org/10.48550/arxiv.2603.28652","title":"Mitigating Backdoor Attacks in Federated Learning Using PPA and MiniMax Game Theory","display_name":"Mitigating Backdoor Attacks in Federated Learning Using PPA and MiniMax Game Theory","publication_year":2026,"publication_date":"2026-03-30","ids":{"openalex":"https://openalex.org/W7148115669","doi":"https://doi.org/10.48550/arxiv.2603.28652"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.28652","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28652","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.2603.28652","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001753157","display_name":"Osama Wehbi","orcid":"https://orcid.org/0000-0002-2121-218X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wehbi, Osama","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029454859","display_name":"Sarhad Arisdakessian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arisdakessian, Sarhad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069036268","display_name":"Omar Abdel Wahab","orcid":"https://orcid.org/0000-0002-3991-4673"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wahab, Omar Abdel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009407218","display_name":"Anderson R. Avila","orcid":"https://orcid.org/0000-0002-3088-5116"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Avila, Anderson","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005269467","display_name":"Azzam Mourad","orcid":"https://orcid.org/0000-0001-9434-5322"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mourad, Azzam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5049846737","display_name":"Hadi Otrok","orcid":"https://orcid.org/0000-0002-9574-5384"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Otrok, Hadi","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":0.7620000243186951,"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":0.7620000243186951,"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":0.04500000178813934,"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":0.01730000041425228,"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/backdoor","display_name":"Backdoor","score":0.9782000184059143},{"id":"https://openalex.org/keywords/reputation","display_name":"Reputation","score":0.5412999987602234},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.45989999175071716},{"id":"https://openalex.org/keywords/incentive","display_name":"Incentive","score":0.45559999346733093},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4300999939441681},{"id":"https://openalex.org/keywords/harm","display_name":"Harm","score":0.39989998936653137},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.3977000117301941},{"id":"https://openalex.org/keywords/trojan","display_name":"Trojan","score":0.35510000586509705}],"concepts":[{"id":"https://openalex.org/C2781045450","wikidata":"https://www.wikidata.org/wiki/Q254569","display_name":"Backdoor","level":2,"score":0.9782000184059143},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7405999898910522},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.6215999722480774},{"id":"https://openalex.org/C48798503","wikidata":"https://www.wikidata.org/wiki/Q877546","display_name":"Reputation","level":2,"score":0.5412999987602234},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.45989999175071716},{"id":"https://openalex.org/C29122968","wikidata":"https://www.wikidata.org/wiki/Q1414816","display_name":"Incentive","level":2,"score":0.45559999346733093},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4300999939441681},{"id":"https://openalex.org/C2777363581","wikidata":"https://www.wikidata.org/wiki/Q15098235","display_name":"Harm","level":2,"score":0.39989998936653137},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3977000117301941},{"id":"https://openalex.org/C174333608","wikidata":"https://www.wikidata.org/wiki/Q19635","display_name":"Trojan","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C177142836","wikidata":"https://www.wikidata.org/wiki/Q44455","display_name":"Game theory","level":2,"score":0.35499998927116394},{"id":"https://openalex.org/C124119293","wikidata":"https://www.wikidata.org/wiki/Q6046081","display_name":"Interdiction","level":2,"score":0.33959999680519104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32670000195503235},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C2779267917","wikidata":"https://www.wikidata.org/wiki/Q170028","display_name":"Deception","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C2776654903","wikidata":"https://www.wikidata.org/wiki/Q2601463","display_name":"SAFER","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2554999887943268}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.28652","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28652","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.2603.28652","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28652","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","score":0.6956239938735962,"id":"https://metadata.un.org/sdg/16"}],"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],"is":[3,50,82],"witnessing":[4],"wider":[5],"adoption":[6],"due":[7],"to":[8,11,59,74,83,126,155,165,183,223],"its":[9,24],"ability":[10],"benefit":[12],"from":[13,29],"large":[14],"amounts":[15],"of":[16,40,47,53,79,100,130,150,190],"scattered":[17],"data":[18,67],"while":[19,72,247],"preserving":[20],"privacy.":[21],"However,":[22],"despite":[23],"advantages,":[25],"federated":[26,140],"learning":[27,141],"suffers":[28],"several":[30],"setbacks":[31,49],"that":[32,215],"directly":[33],"impact":[34,189],"the":[35,38,41,51,61,85,96,101,128,134,148,188,194,200,218],"accuracy,":[36,136],"and":[37,98,119,157,169,173,186,207,236,245],"integrity":[39,97],"global":[42,62,86,102,195],"model":[43,63,87,103,124],"it":[44],"produces.":[45],"One":[46],"these":[48],"presence":[52],"malicious":[54,171,191],"clients":[55,81,132,192],"who":[56],"actively":[57],"try":[58],"harm":[60],"by":[64],"injecting":[65],"backdoor":[66,219],"into":[68,88],"their":[69],"local":[70],"models":[71,177],"trying":[73],"evade":[75],"detection.":[76],"The":[77,122],"objective":[78],"such":[80,111,131],"trick":[84],"making":[89],"false":[90],"predictions":[91],"during":[92],"inference,":[93],"thereby":[94],"compromising":[95],"trustworthiness":[99],"on":[104,133,193,199],"which":[105,238],"honest":[106,167],"stakeholders":[107],"rely.":[108],"To":[109],"mitigate":[110],"mischievous":[112],"behavior,":[113,160,172],"we":[114],"propose":[115],"FedBBA":[116,216],"(Federated":[117],"Backdoor":[118],"Behavior":[120],"Analysis).":[121],"proposed":[123],"aims":[125],"dampen":[127],"effect":[129],"final":[135],"creating":[137],"more":[138],"resilient":[139],"environments.":[142],"We":[143],"engineer":[144],"our":[145],"approach":[146],"through":[147],"combination":[149],"(1)":[151],"a":[152],"reputation":[153],"system":[154],"evaluate":[156],"track":[158],"client":[159],"(2)":[161],"an":[162],"incentive":[163],"mechanism":[164],"reward":[166],"participation":[168],"penalize":[170],"(3)":[174],"game":[175],"theoretical":[176],"with":[178],"projection":[179],"pursuit":[180],"analysis":[181],"(PPA)":[182],"dynamically":[184],"identify":[185],"minimize":[187],"model.":[196],"Extensive":[197],"simulations":[198],"German":[201],"Traffic":[202,209],"Sign":[203,210],"Recognition":[204],"Benchmark":[205],"(GTSRB)":[206],"Belgium":[208],"Classification":[211],"(BTSC)":[212],"datasets":[213],"demonstrate":[214],"reduces":[217],"attack":[220,228,240],"success":[221,241],"rate":[222],"approximately":[224],"1.1%--11%":[225],"across":[226],"various":[227],"scenarios,":[229],"significantly":[230],"outperforming":[231],"state-of-the-art":[232],"defenses":[233],"like":[234],"RDFL":[235],"RoPE,":[237],"yielded":[239],"rates":[242],"between":[243],"23%":[244],"76%,":[246],"maintaining":[248],"high":[249],"normal":[250],"task":[251],"accuracy":[252],"(~95%--98%).":[253]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}