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。