Over the past few years, Federated Learning (FL) has become a popular distributed machine learning paradigm. FL involves a group of clients with decentralized data who collaborate to learn a common model under the coordination of a centralized server, with the goal of protecting clients' privacy by ensuring that local datasets never leave the clients and that the server only performs model aggregation. However, in realistic scenarios, the server may be able to collect a small amount of data that approximately mimics the population distribution and has stronger computational ability to perform the learning process. To address this, we focus on the hybrid FL framework in this paper. While previous hybrid FL work has shown that the alternative training of clients and server can increase convergence speed, it has focused on the scenario where clients fully participate and ignores the negative effect of partial participation. In this paper, we provide theoretical analysis of hybrid FL under clients' partial participation to validate that partial participation is the key constraint on convergence speed. We then propose a new algorithm called FedCLG, which investigates the two-fold role of the server in hybrid FL. Firstly, the server needs to process the training steps using its small amount of local datasets. Secondly, the server's calculated gradient needs to guide the participated clients' training and the server's aggregation. We validate our theoretical findings through numerical experiments, which show that our proposed method FedCLG outperforms state-of-the-art methods.

翻译：近年来，联邦学习（Federated Learning, FL）已成为一种流行的分布式机器学习范式。FL涉及一组拥有去中心化数据的客户端，它们在一台中央服务器的协调下协作学习一个共同模型，其目标是通过确保本地数据集始终保留在客户端本地、服务器仅执行模型聚合来保护客户隐私。然而，在实际场景中，服务器可能能够收集到少量近似反映总体分布的数据，并具有更强的计算能力来执行学习过程。为解决这一问题，本文聚焦于混合联邦学习框架。尽管先前的混合FL研究表明，客户端与服务器的交替训练可提升收敛速度，但其研究情境均假设客户端完全参与，并忽视了部分参与的负面影响。本文针对客户端部分参与情境下的混合FL进行理论分析，以验证部分参与是收敛速度的关键限制因素。随后，我们提出一种新算法FedCLG，该算法探究了服务器在混合FL中的双重作用：首先，服务器需利用其少量本地数据集执行训练步骤；其次，服务器计算得到的梯度需指导参与客户端的训练及服务器的聚合过程。通过数值实验验证了我们的理论发现，结果表明所提出的FedCLG方法优于现有最先进方法。