Federated Learning is a machine learning paradigm where we aim to train machine learning models in a distributed fashion. Many clients/edge devices collaborate with each other to train a single model on the central. Clients do not share their own datasets with each other, decoupling computation and data on the same device. In this paper, we propose yet another adaptive federated optimization method and some other ideas in the field of federated learning. We also perform experiments using these methods and showcase the improvement in the overall performance of federated learning.

翻译：联邦学习是一种机器学习模式,我们的目标是以分布式方式培训机器学习模式,许多客户/尖端设备相互协作,在中央一级培训单一模式,客户不相互共享自己的数据集,不将计算和数据分离在同一设备上。在本文件中,我们提出了另一个适应性联合优化方法和联邦学习领域的其他一些想法。我们还利用这些方法进行实验,并展示了联邦学习总体绩效的改善。