AI-powered eVolution towards opEn and secuRe edGe architEctures

VERGE将从“edge for AI”、“AI for edge”以及AI for edge的安全性、隐私性和可信赖性三个方面来解决边缘计算的演变问题。“Edge for AI”定义了一个灵活、模块化和融合的Edge平台，可以支持边缘的分布式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，实现动态功能放置。特定于应用程序的网络和计算kpi将以高效和无冲突的方式得到保证，同时考虑到边缘资源的限制。解决了AI for Edge的安全性，隐私性和可信赖性，以确保基于AI的模型免受对抗性攻击的安全性，数据和模型的隐私性，以及通过提供模型决策的解释来提高模型的信任，从而确保培训和执行的透明度。VERGE将通过在两个用例中提供7个演示来验证这三个观点：xr驱动的边缘支持工业B5G应用在土耳其的两个独立arelik站点，以及边缘辅助的自动电车在佛罗伦萨的运营。VERGE将通过与相关标准化机构和开放资源的联系和贡献，将结果传播给学术界、工业界和更广泛的利益相关者社区，通过trl进行一系列演示，并创建一个开放的数据空间，使公众能够访问项目生成的数据集。

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.