With mobile, IoT and sensor devices becoming pervasive in our life and recent advances in Edge Computational Intelligence (e.g., Edge AI/ML), it became evident that the traditional methods for training AI/ML models are becoming obsolete, especially with the growing concerns over privacy and security. This work tries to highlight the key challenges that prohibit Edge AI/ML from seeing wide-range adoption in different sectors, especially for large-scale scenarios. Therefore, we focus on the main challenges acting as adoption barriers for the existing methods and propose a design with a drastic shift from the current ill-suited approaches. The new design is envisioned to be model-centric in which the trained models are treated as a commodity driving the exchange dynamics of collaborative learning in decentralized settings. It is expected that this design will provide a decentralized framework for efficient collaborative learning at scale.

翻译：随着移动设备、物联网和传感器设备在我们的日常生活中变得无处不在，以及边缘计算智能（例如边缘AI/ML）的最新进展，传统的AI/ML模型训练方法已明显变得过时，尤其是在隐私和安全问题日益受到关注的背景下。本文旨在强调阻碍边缘AI/ML在不同领域（特别是大规模场景）中广泛采用的关键挑战。为此，我们聚焦于当前方法中作为采用障碍的主要挑战，并提出一种设计，该设计与现有不适用的方法截然不同。新设计以模型为中心，将训练好的模型视为一种商品，驱动去中心化环境中协作学习的交换动态。预计该设计将提供一个去中心化的框架，用于实现大规模高效的协作学习。