2024/04/19 by Zeke Xia, Xia, Zeke, Ming Hu +13 · 1 citation
Computer Science · Psychology · #Artificial intelligence #Asynchronous communication #Asynchronous learning #Balance (ability) #Cache #Caching and Content Delivery #Computer network #Computer science #Cooperative Communication and Network Coding #Distributed #Distributed computing #FOS: Computer and information sciences #Feature (linguistics) #Federated learning #Machine Learning (cs.LG) #Parallel #Privacy-Preserving Technologies in Data #Psychology #Synchronous learning #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2404.12850
openalex publication_date 2024/04/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.