Current distributed learning systems predominantly rely on artificial neural networks, which are generally energy-intensive. This issue is further exacerbated with the use of more advanced and larger models. In contrast, brain-inspired neuromorphic learning algorithms, such as spiking neural networks (SNNs), are renowned for their energy efficiency, making them particularly promising for low-power edge applications. However, research on integrating SNNs with distributed learning remains scarce, and these paradigms are not yet optimized for wireless edge environments. This project aims to advance the fundamental understanding of distributed SNN learning by addressing several unique and challenging questions: 1) How to overcome the constraints of system memory and communication bandwidth, along with system and adversarial perturbations due to channel instability and openness in wireless edge environments, impacting distributed learning? Can distributed SNNs provide advantageous solutions in those cases? 2) How to achieve the joint optimization of efficiency, robustness, and utility for distributed SNN learning over wireless edge environments? The anticipated outcome of this project contributes to unleashing the full potential of wireless edge artificial intelligence systems in various applications, including power systems and environmental monitoring, intelligent healthcare and manufacturing, and collaborative robotics. Additionally, the investigator is committed to immersing g