Recent advancements in edge computing have significantly enhanced the AI capabilities of Internet of Things (IoT) devices. However, these advancements introduce new challenges in knowledge exchange and resource management, particularly addressing the spatiotemporal data locality in edge computing environments. This study examines algorithms and methods for deploying distributed machine learning within autonomous, network-capable, AI-enabled edge devices. We focus on determining confidence levels in learning outcomes considering the spatial variability of data encountered by independent agents. Using collaborative mapping as a case study, we explore the application of the Distributed Neural Network Optimization (DiNNO) algorithm extended with Bayesian neural networks (BNNs) for uncertainty estimation. We implement a 3D environment simulation using the Webots platform to simulate collaborative mapping tasks, decouple the DiNNO algorithm into independent processes for asynchronous network communication in distributed learning, and integrate distributed uncertainty estimation using BNNs. Our experiments demonstrate that BNNs can effectively support uncertainty estimation in a distributed learning context, with precise tuning of learning hyperparameters crucial for effective uncertainty assessment. Notably, applying Kullback-Leibler divergence for parameter regularization resulted in a 12-30% reduction in validation loss during distributed BNN training compared to other regularization strategies.

翻译：近年来，边缘计算的进步显著增强了物联网设备的AI能力。然而，这些进展在知识交换与资源管理方面引入了新的挑战，特别是如何应对边缘计算环境中的时空数据局部性问题。本研究探讨了在自主、具备网络能力且支持AI的边缘设备内部署分布式机器学习算法与方法。我们重点关注在考虑独立智能体所遇数据空间变异性的前提下，如何确定学习结果的置信水平。以协同建图作为案例研究，我们探索了采用贝叶斯神经网络扩展的分布式神经网络优化算法在不确定性估计中的应用。我们利用Webots平台实现三维环境仿真以模拟协同建图任务，将DiNNO算法解耦为独立进程以支持分布式学习中的异步网络通信，并整合了基于BNN的分布式不确定性估计方法。实验表明，BNN能有效支持分布式学习场景中的不确定性估计，其中学习超参数的精确调优对于有效的不确定性评估至关重要。值得注意的是，在分布式BNN训练中，应用Kullback-Leibler散度进行参数正则化相比其他正则化策略，使验证损失降低了12-30%。