Initially considered as low-power units with limited autonomous processing,
Edge IoT devices have seen a paradigm shift with the introduction of FPGAs and
AI accelerators. This advancement has vastly amplified their computational
capabilities, emphasizing the practicality of edge AI. Such progress introduces
new challenges of optimizing AI tasks for the limitations of energy and network
resources typical in Edge computing environments. Our study explores methods
that enable distributed data processing through AI-enabled edge devices,
enhancing collaborative learning capabilities. A key focus of our research is
the challenge of determining confidence levels in learning outcomes,
considering the spatial and temporal variability of data sets encountered by
independent agents. To address this issue, we investigate the application of
Bayesian neural networks, proposing a novel approach to manage uncertainty in
distributed learning environments.

利用 FPGAs 和 AI 加速器，边缘物联网设备实现了计算能力的大幅提升，研究探讨了通过 AI 设备实现分布式数据处理的方法，并提出了在分布式学习环境中管理不确定性的一种新方法，即贝叶斯神经网络。