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.

本文提出了一种名为 FedCLG 的新算法，它可以在类似于混合 FL 的情形下防止部分参与的限制，同时保护客户隐私并实现模型聚合。数值实验表明，该算法胜过现有方法。