论文标题
最佳统计率和隐私保证的拜占庭式联盟学习
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees
论文作者
论文摘要
我们提出了几乎最佳的统计率的拜占庭式联合学习方案。与先前的工作相反，我们提出的协议改善了维度的依赖性，并在强烈凸出损失的所有参数方面达到了严格的统计率。我们对竞争方案进行基准测试，并显示提出的协议的经验优势。最后，我们指出，我们的带有存储桶的协议可以自然与保密保证程序相结合，以针对半honest服务器引入安全性。评估代码在https://github.com/wanglun1996/secure-robust-federated-learning中提供。
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.
