  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.
