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.

通过开发并建立新的方法，KDT NEUROKIT2E 项目旨在为边缘设备上的人工智能应用提供新的开源框架，其中包括量化、剪枝感知训练和稀疏化等创新技术，以显著扩展此类设备的功能范围，利用本地资源来处理复杂的机器学习任务，为创新的学习方法奠定基础。