Federated learning (FL) offers a solution to train a global machine learning model while still maintaining data privacy, without needing access to data stored locally at the clients. However, FL suffers performance degradation when client data distribution is non-IID, and a longer training duration to combat this degradation may not necessarily be feasible due to communication limitations. To address this challenge, we propose a new adaptive training algorithm $\texttt{AdaFL}$, which comprises two components: (i) an attention-based client selection mechanism for a fairer training scheme among the clients; and (ii) a dynamic fraction method to balance the trade-off between performance stability and communication efficiency. Experimental results show that our $\texttt{AdaFL}$ algorithm outperforms the usual $\texttt{FedAvg}$ algorithm, and can be incorporated to further improve various state-of-the-art FL algorithms, with respect to three aspects: model accuracy, performance stability, and communication efficiency.

本文提出了一种自适应训练算法AdaFL，该算法通过注意力机制和动态分数方法来平衡性能稳定性和通信效率，实验结果表明相对于FedAvg算法，AdaFL算法在模型精度、性能稳定性和通信效率三个方面都有很大的提高。

基于动态注意力的通信高效联邦学习