CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance

arXiv:2404.12850v1 Announce Type: new
Abstract: 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 …

abstract aiot applications artificial artificial intelligence arxiv asynchronous balance cache capability cs.dc cs.lg data devices distributed efficiency feature federated learning hierarchical however inference intelligence machine machine learning massive paradigm type via