Heterogeneous Federated Learning for Non-IID Smartwatch Data Classification
Jia-Hao Syu, Jerry Chun‐Wei Lin · IEEE Internet of Things Journal · 2024
In this article, we propose a heterogeneous federated learning for classification (HFLC) model, which divides features into sensitive and nonsensitive for the privacy concerns. The local-network is first trained on all features to achieve specificity, and the global-network is then trained on nonsensitive features. They are are the carriers of heterogeneous federated learning (HFL) to achieve generalization. The designed HFL is then conducted on global-networks to deal with the non-IID distributions of features and labels. Experimental results show that the proposed HFLC systems reduce the classification error from 29.5% to 36.7% compared to the traditional federated average; from 17.7% to 21.4% compared to existing heterogeneous federated algorithms, and from 2.2% to 5.0% compared to state-of-the-art systems.