HierFLMC: Efficient hierarchical federated learning based on soft clustering model compression

Federated Learning (FL) is an advanced distributed machine learning paradigm that enables clients to collaboratively train a shared neural network model using local datasets, transmitting model parameters instead of raw data. However, in many FL systems, the frequent exchange of model parameters between clients and remote cloud servers leads to significant communication overhead. As the model size increases, existing FL methods incur substantial communication costs. To address this bottleneck, this paper proposes a novel hierarchical federated learning model compression scheme (HierFLMC). This scheme integrates model compression techniques within a hierarchical framework, significantly enhancing communication efficiency between edge devices and cloud servers. In addition, an innovative preliminary soft clustering model update compression algorithm (SCMUC) is proposed. The SCMUC algorithm utilizes the K-means method for initial clustering, effectively reducing the computational complexity associated with traditional soft clustering methods. We validate the proposed scheme using the CIFAR-10 and FEMNIST datasets, demonstrating a 2 % improvement in model accuracy and an 11 % reduction in communication time compared to MUCSC. Experimental results indicate that this approach not only achieves a favorable compression rate but also substantially improves communication efficiency.