High-Precision Cluster Federated Learning for Smart Home: An Edge-Cloud Collaboration Approach

Owing to the strong protection of data privacy, federated learning (FL) has become a key method to achieve intelligent decision making in smart homes. However, under realistic conditions, such as differentiated requirements and heterogeneous service environments, FL in smart homes faces the problems of non-independent and identically distributed (non-IID) data and uneven computing power, which lead to the poor adaptability of global models. To address this issue, this study proposes a cluster FL architecture based on edge-cloud collaboration. Firstly, a Gaussian mixture model-based cluster FL is proposed to improve the model accuracy by clustering features on the FL dataset and ensuring an independent identical distribution of the data. Subsequently, a model training strategy based on edge-cloud collaboration is proposed to achieve the sharing of edge-cloud computing power by split learning, which provides sufficient computing power for model training. The simulation results show that the proposed architecture improves the accuracy of global models, meanwhile ensures the provision of normal network service.