论文标题
联合多税：在非IID环境中用于联合学习的有效培训方法
Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments
论文作者
论文摘要
联合学习面临着绩效和网络通信挑战，尤其是在数据不是独立且在客户端分布（IID）的环境中。为了应对以前的挑战，我们介绍了以联邦为中心化的一致性属性，并表明联合的单尼局部训练方法可以达到与非IID环境中相应的集中式培训相当的性能。为了应对后者，我们介绍了联合的多米尼批次方法，并说明它可以在绩效和沟通效率和跨越非IID设置平均的绩效和沟通效率之间建立权衡。
Federated learning has faced performance and network communication challenges, especially in the environments where the data is not independent and identically distributed (IID) across the clients. To address the former challenge, we introduce the federated-centralized concordance property and show that the federated single-mini-batch training approach can achieve comparable performance as the corresponding centralized training in the Non-IID environments. To deal with the latter, we present the federated multi-mini-batch approach and illustrate that it can establish a trade-off between the performance and communication efficiency and outperforms federated averaging in the Non-IID settings.
