Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training. However, new approaches to FL often discuss their contributions involving small deep-learning models only. With the tremendous success of transformer models, the following question arises: What is necessary to operationalize foundation models in an FL application? Knowing that computation and communication often take up similar amounts of time in FL, we introduce a novel taxonomy focused on computational and communication efficiency methods in FL applications. This said, these methods aim to optimize the training time and reduce communication between clients and the server. We also look at the current state of widely used FL frameworks and discuss future research potentials based on existing approaches in FL research and beyond.

Federated Learning (FL)的研究论文主要讨论了在隐私保护的协作训练中应用小型深度学习模型的新方法以及在FL应用中操作基于transformer模型的必要性，介绍了一种专注于FL应用中计算和通信效率方法的新的分类法，并探讨了当前广泛使用的FL框架的现状以及基于现有FL研究方法的未来研究潜力。

基于高效联邦学习方法的基础模型训练调研