Federated Learning (FL) is a machine-learning approach enabling collaborative model training across multiple decentralized edge devices that hold local data samples, all without exchanging these samples. This collaborative process occurs under the supervision of a central server orchestrating the training or via a peer-to-peer network. The significance of FL is particularly pronounced in industries such as healthcare and finance, where data privacy holds paramount importance. However, training a model under the Federated learning setting brings forth several challenges, with one of the most prominent being the heterogeneity of data distribution among the edge devices. The data is typically non-independently and non-identically distributed (non-IID), thereby presenting challenges to model convergence. This report delves into the issues arising from non-IID and heterogeneous data and explores current algorithms designed to address these challenges.

联邦学习是一种机器学习方法，可以在持有本地数据样本的多个分散边缘设备之间进行协作模型训练，而无需交换这些样本。本报告探讨了非独立非同分布的数据与异构数据带来的问题，并探索了目前设计用于解决这些挑战的算法。

对应用于解决联邦学习中非独立同分布和异质数据特性的不同技术的综述