Federated learning (FL) is a distributed machine learning framework where the
global model of a central server is trained via multiple collaborative steps by
participating clients without sharing their data. While being a flexible
framework, where the distribution of local data, participation rate, and
computing power of each client can greatly vary, such flexibility gives rise to
many new challenges, especially in the hyperparameter tuning on both the server
and the client side. We propose $\Delta$-SGD, a simple step size rule for SGD
that enables each client to use its own step size by adapting to the local
smoothness of the function each client is optimizing. We provide theoretical
and empirical results where the benefit of the client adaptivity is shown in
various FL scenarios. In particular, our proposed method achieves TOP-1
accuracy in 73% and TOP-2 accuracy in 100% of the experiments considered
without additional tuning.

提出了一种名为 Delta-SGD 的算法用于优化联邦学习模型中的超参数，该算法能够在各个客户端之间自适应优化不同的参数，实验结果表明，该算法可以在不需要额外调整的情况下，在 73% 的实验中达到 TOP-1 准确度，在 100% 的实验中达到 TOP-2 准确度。