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.

翻译：最初被视为算力有限、自主处理能力低下的低功耗单元，边缘物联网设备随着FPGA和AI加速器的引入经历了范式转变。这一进步极大地提升了其计算能力，凸显了边缘AI的实用性。这种进展带来了新的挑战：如何在边缘计算环境典型的能源与网络资源限制下优化AI任务。本研究探索了通过支持AI的边缘设备实现分布式数据处理的方法，从而增强协作学习能力。我们研究的核心挑战在于，考虑到独立智能体所处理数据集的时空变异性，如何确定学习结果的置信度水平。针对该问题，我们研究了贝叶斯神经网络的应用，提出了一种在分布式学习环境中管理不确定性的新方法。