FedTune-SGM: A Stackelberg-Driven Personalized Federated Learning Strategy for Edge Networks

Abstract

Federated Learning (FL) has emerged as a prominent solution for distributed learning environments, enabling collaborative model training without centralized data collection. However, FL faces significant challenges such as data heterogeneity and resource-constraint edge devices for model training and analysis, leading to accuracy degradation and bias in model performance. To address these critical issues, we propose a novel FL strategy named FedTune-SGM, designed to optimize model training in decentralized settings. In this strategy, a cloud-based model is initially trained and fine-tuned on the edge devices with additional layers tailored to the specific data characteristics. This fine-tuning process effectively mitigates the impact of data heterogeneity, enhancing the robustness and generalization capability of the model. FedTune-SGM employs a strategic weighting mechanism that ensures a balanced and equitable contribution from participating edge devices to prevent dominant influences from resource-rich devices and promote a fairer and more accurate aggregated model. Additionally, the proposed strategy integrates a Stackelberg Game model to foster an interactive and dynamic cloud-edge setup that motivates edge devices to invest more effort in model training and ensures the effectiveness of resource-constraint edge devices. Extensive experiments conducted on three diverse datasets highlight the superior performance of the proposed FedTune-SGM strategy compared to state-of-the-art FL techniques in terms of accuracy and robustness while meeting the critical challenges of data heterogeneity and resource limitations in FL environments. Through these innovations, FedTune-SGM paves the way for more reliable and efficient distributed learning systems, unlocking the full potential of FL in practical applications.