Adaptive Model Compression for Efficient Federated Learning in IoT Systems

Federated learning (FL), as an emerging collaborative learning paradigm, offers a promising solution to tackle learning challenges in IoT environments. However, the inherent mechanism of FL often results in significant communication overhead, which poses challenges in resource-constrained IoT systems. Moreover, most existing model compression methods struggle to balance model accuracy and compression ratio effectively with low resource consumption: excessive compression degrades accuracy, while insufficient compression incurs high communication overhead. To address this issue, we propose adaMC, an adaptive model compression algorithm for FL in IoT systems. This method integrates two types of network models: main and auxiliary networks. The auxiliary networks generate optimal sparsification strategies to guide the corresponding layers in the main network to become sparse. To ensure robust performance, we develop a bias correction method that guarantees both model accuracy and convergence, accompanied by a theoretical analysis. Experimental results on widely used deep learning models and public datasets demonstrate that our adaMC method is effective, achieving competitive performance compared to FL without compression.