Federated learning is an emerging distributed machine learning method, enables a large number of clients to train a model without exchanging their local data. The time cost of communication is an essential bottleneck in federated learning, especially for training large-scale deep neural networks. Some communication-efficient federated learning methods, such as FedAvg and FedAdam, share the same learning rate across different clients. But they are not efficient when data is heterogeneous. To maximize the performance of optimization methods, the main challenge is how to adjust the learning rate without hurting the convergence. In this paper, we propose a heterogeneous local variant of AMSGrad, named FedLALR, in which each client adjusts its learning rate based on local historical gradient squares and synchronized learning rates. Theoretical analysis shows that our client-specified auto-tuned learning rate scheduling can converge and achieve linear speedup with respect to the number of clients, which enables promising scalability in federated optimization. We also empirically compare our method with several communication-efficient federated optimization methods. Extensive experimental results on Computer Vision (CV) tasks and Natural Language Processing (NLP) task show the efficacy of our proposed FedLALR method and also coincides with our theoretical findings.

我们提出了一种名为FedLALR的异构本地AMSGrad变体，该方法通过基于本地历史梯度平方和同步学习率来调整每个客户端的学习率，理论分析表明我们的方法具有良好的收敛性和与客户端数量线性加速，这为联邦优化提供了可扩展性。我们还通过与其他几种通信高效的联邦优化方法进行了实证比较，在计算机视觉和自然语言处理任务上的广泛实验结果显示我们提出的FedLALR方法的有效性，并与我们的理论发现相吻合。

FedLALR：面向客户端的自适应学习速率实现非独立同分布数据的线性加速