Accuracy and Performance of Machine Learning Methodologies

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 \textbf{Ca}che-based aggregation mechanism and a feature \textbf{Ba}lance-guided device selection strategy.