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ML×2Crypto×2Distributed×2Game Theory×1
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2026
FL-PBM: Pre-Training Backdoor Mitigation for Federated Learning
The paper proposes FL-PBM, a novel pre-training defense mechanism for federated learning that proactively filters poisoned data using a multi-stage process, significantly reducing backdoor attack success rates while maintaining high model accuracy.
Mitigating Backdoor Attacks in Federated Learning Using PPA and MiniMax Game Theory
The paper proposes FedBBA, a robust defense mechanism combining reputation systems, incentive mechanisms, and PPA-based game theory, to significantly mitigate backdoor attacks in Federated Learning.
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