AI-powered eVolution towards opEn and secuRe edGe architEctures

VERGE将从三个方面解决边缘计算的发展：“边缘为AI”，“AI为边缘”以及边缘AI的安全性，隐私性和可信性。“Edge for AI”定义了一个灵活、模块化和融合的边缘平台，可以在边缘支持分布式AI。这是通过统一云原生应用程序、MEC和网络服务的生命周期管理和闭环自动化来实现的，同时充分利用多核和多加速器功能来实现超高计算性能。“AI for Edge”通过管理和协调底层物理、网络和计算资源，实现动态功能布局。应用程序特定的网络和计算KPI将以高效和无冲突的方式得到保证，并考虑到边缘资源约束。Edge AI的安全性、隐私性和可信度旨在确保基于AI的模型的安全性，以防止对抗性攻击，数据和模型的隐私性，以及通过为模型决策提供解释来提高模型信任度的训练和执行透明度。VERGE将通过在两个用例中提供7个演示来验证这三个观点-在土耳其的两个独立的Arçelik站点上使用XR驱动的Edge启用的工业B5 G应用，以及在佛罗伦萨的Edge辅助自动有轨电车运营。VERGE将通过与相关标准化机构和开放源的联络和贡献，向学术界、工业界和更广泛的利益攸关方社区传播成果，通过一系列演示展示通过TRL取得的进展，并创建一个开放的数据空间，使公众能够访问该项目生成的数据集。

VERGE will tackle evolution of edge computing from three perspectives: “Edge for AI”, “AI for Edge” and security, privacy and trustworthiness of AI for Edge. “Edge for AI” defines a flexible, modular and converged Edge platform that isready to support distributed AI at the edge. This is achieved by unifying lifecycle management and closed-loop automation for cloud-native applications, MEC and network services, while fully exploiting multi-core and multi-accelerator capabilities for ultra-high computational performance. “AI for Edge” enables dynamic function placement by managing and orchestrating the underlying physical, network, and compute resources. Application-specific network and computational KPIs will be assured in an efficient and collision-free manner, taking Edge resource constraints in to account. Security, privacy and trustworthiness of AI for Edge are addressed to ensure security of the AI-based models against adversarial attacks, privacy of data and models, and transparency in training and execution by providing explanations for model decisions improving trust in models. VERGE will verify the three perspectives through delivery of 7 demonstrations across two use cases - XR-driven Edge-enabled industrial B5G applications across two separate Arçelik sites in Turkey, and Edge-assisted Autonomous Tram operation in Florence. VERGE will disseminate results to academia, industry and the wider stakeholder community through liaisons and contributions to relevant standardization bodies and open sources, a series of demonstrations showing progression through TRLs and by creating an open dataspace for enabling public access to the datasets generated by the project.