Machine Learning (ML) is increasingly used to drive the operation of complex distributed systems deployed on the cloud-edge continuum enabled by 5G. Correspondingly, distributed systems' behavior is becoming more non-deterministic in nature. This evolution of distributed systems requires the definition of new assurance approaches for the verification of non-functional properties. Certification, the most popular assurance technique for system and software verification, is not immediately applicable to systems whose behavior is determined by Machine Learning-based inference. However, there is an increasing push from policy makers, regulators, and industrial stakeholders towards the definition of techniques for the certification of non-functional properties (e.g., fairness, robustness, privacy) of ML. This article analyzes the challenges and deficiencies of current certification schemes, discusses open research issues and proposes a first certification scheme for ML-based distributed systems.

翻译：机器学习（ML）正日益被用于驱动部署在5G使能的云-边缘连续体上的复杂分布式系统的运行。相应地，分布式系统的行为正变得愈发非确定性。这种分布式系统的演进要求定义新的保障方法，用于验证其非功能属性。认证作为系统和软件验证中最流行的保障技术，并不能直接应用于行为由机器学习推理决定的系统。然而，政策制定者、监管机构和行业利益相关者正日益推动定义用于认证机器学习非功能属性（如公平性、鲁棒性、隐私性）的技术。本文分析了当前认证方案的挑战与不足，探讨了开放研究问题，并提出了一种面向ML驱动的分布式系统的首个认证方案。