Sparse Communication Mechanism for Federated Learning in IoT Systems
Xi Zhu, Junbo Wang, Zhi Liu, Zibin Zheng · IEEE Internet of Things Journal · 2025
Federated learning (FL), an emerging distributed learning paradigm, addresses challenges in decentralized environments, particularly in internet of things (IoT) networks where numerous devices generate vast amounts of private data. A major concern in FL for IoT systems is communication overhead, especially in resource-constrained and wireless environments where frequent model uploads for aggregation create a critical bottleneck. As the complexity of neural networks increases, traditional FL methods demand substantial communication resources, which limits scalability in IoT applications. To address this, we propose a novel sparse communication mechanism for FL, called FedSC, that achieves high generalization performance and accuracy under extremely low communication frequencies, particularly for non-IID data. On the client side, we propose a multi-level model compression mechanism to capture and retain important information from the trained local model, which is then uploaded to the server. On the server side, we propose an iterative extraction mechanism to reconstruct models of uniform size based on the client models. Each extraction is followed by an aggregation step in an iterative process, ensuring effective generalization with non-IID data. As a result, clients need to upload their models only 1-5 times after completing local training, significantly reducing the communication overhead compared to traditional FL, which requires continuous uploads throughout the entire training process. Simulations on public datasets with popular deep learning models demonstrate that FedSC reduces the number of model uploads from hundreds to just 1-5 times while maintaining high accuracy, highlighting its potential to significantly enhance communication efficiency for FL in IoT systems.