Traditionally, IoT edge devices have been perceived primarily as low-power components with limited capabilities for autonomous operations. Yet, with emerging advancements in embedded AI hardware design, a foundational shift paves the way for future possibilities. Thus, the aim of the KDT NEUROKIT2E project is to establish a new open-source framework to further facilitate AI applications on edge devices by developing new methods in quantization, pruning-aware training, and sparsification. These innovations hold the potential to expand the functional range of such devices considerably, enabling them to manage complex Machine Learning (ML) tasks utilizing local resources and laying the groundwork for innovative learning approaches. In the context of 6G's transformative potential, distributed learning among independent agents emerges as a pivotal application, attributed to 6G networks' support for ultra-reliable low-latency communication, enhanced data rates, and advanced edge computing capabilities. Our research focuses on the mechanisms and methodologies that allow edge network-enabled agents to engage in collaborative learning in distributed environments. Particularly, one of the key issues within distributed collaborative learning is determining the degree of confidence in the learning results, considering the spatio-temporal locality of data sets perceived by independent agents.

翻译：传统上，物联网边缘设备主要被视为自主操作能力有限的低功耗组件。然而，随着嵌入式人工智能硬件设计的新兴进步，基础性转变正在为未来可能性铺平道路。因此，KDT NEUROKIT2E项目的目标是建立一个新的开源框架，通过开发量化、剪枝感知训练和稀疏化的新方法，进一步促进边缘设备上的AI应用。这些创新有望显著扩展此类设备的功能范围，使其能够利用本地资源管理复杂的机器学习任务，并为创新学习方法奠定基础。在6G变革性潜力的背景下，独立智能体之间的分布式学习成为关键应用，这归因于6G网络对超可靠低延迟通信、更高数据速率和先进边缘计算能力的支持。我们的研究聚焦于使边缘网络智能体能够在分布式环境中进行协作学习的机制和方法。特别地，分布式协作学习中的关键问题之一是在考虑独立智能体感知的数据集时空局部性的情况下，确定学习结果的置信度。