We propose Byzantine-robust federated learning protocols with nearly optimal
statistical rates. In contrast to prior work, our proposed protocols improve
the dimension dependence and achieve a tight statistical rate in terms of all
the parameters for strongly convex losses. We benchmark against competing
protocols and show the empirical superiority of the proposed protocols.
Finally, we remark that our protocols with bucketing can be naturally combined
with privacy-guaranteeing procedures to introduce security against a
semi-honest server. The code for evaluation is provided in
this https URL.

在强凸损失函数方面，通过改进维度依赖性，我们提出了拜占庭安全的联邦学习协议，并取得了接近最优的统计速率，并且通过桶聚合和隐私保护措施的自然结合，增强了安全性。

具有最优统计速率和隐私保证的拜占庭鲁棒联邦学习

Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees

Working under a model of privacy in which data remains private even from the
statistician, we study the tradeoff between privacy guarantees and the utility
of the resulting statistical estimators. We prove bounds on
information-theoretic quantities, including mutual information and
Kullback-Leibler divergence, that depend on the privacy guarantees. When
combined with standard minimax techniques, including the Le Cam, Fano, and
Assouad methods, these inequalities allow for a precise characterization of
statistical rates under local privacy constraints. We provide a treatment of
several canonical families of problems: mean estimation, parameter estimation
in fixed-design regression, multinomial probability estimation, and
nonparametric density estimation. For all of these families, we provide lower
and upper bounds that match up to constant factors, and exhibit new (optimal)
privacy-preserving mechanisms and computationally efficient estimators that
achieve the bounds.

研究在数据即使隐私保护给定的情况下，隐私保证和结果统计估计器的效用之间的权衡，通过信息论和标准最小最大技术，提出本地隐私约束下统计速率的精确刻画，并提出新的隐私保护机制和计算有效的估计器，以实现界限。