  Federated Learning (FL) as a promising distributed machine learning paradigm
has been widely adopted in Artificial Intelligence of Things (AIoT)
applications. However, the efficiency and inference capability of FL is
seriously limited due to the presence of stragglers and data imbalance across
massive AIoT devices, respectively. To address the above challenges, we present
a novel asynchronous FL approach named CaBaFL, which includes a hierarchical
Cache-based aggregation mechanism and a feature Balance-guided device selection
strategy. CaBaFL maintains multiple intermediate models simultaneously for
local training. The hierarchical cache-based aggregation mechanism enables each
intermediate model to be trained on multiple devices to align the training time
and mitigate the straggler issue. In specific, each intermediate model is
stored in a low-level cache for local training and when it is trained by
sufficient local devices, it will be stored in a high-level cache for
aggregation. To address the problem of imbalanced data, the feature
balance-guided device selection strategy in CaBaFL adopts the activation
distribution as a metric, which enables each intermediate model to be trained
across devices with totally balanced data distributions before aggregation.
Experimental results show that compared with the state-of-the-art FL methods,
CaBaFL achieves up to 9.26X training acceleration and 19.71\% accuracy
improvements.
