\(\mathsf {FLOD}\): Oblivious Defender for Private Byzantine-Robust Federated Learning with Dishonest-Majority

Privacy and Byzantine-robustness are two major concerns of federated learning (FL), but mitigating both threats simultaneously is highly challenging: privacy-preserving strategies prohibit access to individual model updates to avoid leakage, while Byzantine-robust methods require access for comprehensive mathematical analysis. Besides, most Byzantine-robust methods only work in the honest-majority setting.