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 https://github.com/wanglun1996/secure-robust-federated-learning.

翻译：我们提出具有近乎最优统计率的拜占庭鲁棒联邦学习协议。与先前工作相比，我们提出的协议改进了维度依赖性，并在强凸损失函数下实现了所有参数层面的紧致统计率。我们与竞争性协议进行基准测试，并实证展示了所提协议的优越性。最后，我们指出采用分桶机制的协议可自然地与隐私保障过程结合，以引入针对半诚实服务器的安全性。评估代码已开源在 https://github.com/wanglun1996/secure-robust-federated-learning。