September 25, 2021

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FLOD: Oblivious Defender for Private Byzantine-Robust Federated Learning with Dishonest-Majority, by Ye Dong and Xiaojun Chen and Kaiyun Li and Dakui Wang and Shuai Zeng

textit{Privacy} and textit{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 textit{honest-majority} setting.

We present $mathsf{FLOD}$, a novel oblivious defender for private Byzantine-robust FL in dishonest-majority setting. Basically, we propose a novel Hamming distance-based aggregation method to resist $>1/2$ Byzantine attacks using a small textit{root-dataset} and textit{server-model} for bootstrapping trust. Furthermore, we employ two non-colluding servers and use additive homomorphic encryption ($mathsf{AHE}$) and secure two-party computation (2PC) primitives to construct efficient privacy-preserving building blocks for secure aggregation, in which we propose two novel in-depth variants of Beaver Multiplication triples (MT) to reduce the overhead of Bit to Arithmetic ($mathsf{Bit2A}$) conversion and vector weighted sum aggregation ($mathsf{VSWA}$) significantly. Experiments on real-world and synthetic datasets demonstrate our effectiveness and efficiency: (romannumeral1) $mathsf{FLOD}$ defeats known Byzantine attacks with a negligible effect on accuracy and convergence, (romannumeral2) achieves a reduction of $approx 2times$ for offline (resp. online) overhead of $mathsf{Bit2A}$ and $mathsf{VSWA}$ compared to $mathsf{ABY}$-$mathsf{AHE}$ (resp. $mathsf{ABY}$-$mathsf{MT}$) based methods (NDSS’15), (romannumeral3) and reduces total online communication and run-time by $167$-$1416times$ and $3.1$-$7.4times$ compared to $mathsf{FLGUARD}$ (Crypto Eprint 2021/025).