论文标题
Fedgrad：分散的机器学习中的优化
FedGrad: Optimisation in Decentralised Machine Learning
论文作者
论文摘要
联合学习是一种机器学习范式，我们旨在以分布式方式训练机器学习模型。许多客户/边缘设备相互协作，在中心训练单个模型。客户端不会彼此共享自己的数据集，在同一设备上分解计算和数据。在本文中，我们提出了另一种自适应联合优化方法以及联合学习领域的其他一些想法。我们还使用这些方法进行实验，并展示联合学习的整体表现的改善。
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.
