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）正越来越多地被用于驱动部署在5G赋能的云-边缘连续体上的复杂分布式系统的运行。相应地，分布式系统的行为在本质上变得更加非确定性。这种分布式系统的演进要求为验证非功能性属性定义新的保证方法。认证作为系统和软件验证中最流行的保证技术，并不能直接适用于行为由机器学习推理所决定的系统。然而，政策制定者、监管机构和工业利益相关者正日益推动定义机器学习非功能性属性（如公平性、鲁棒性、隐私性）的认证技术。本文分析了当前认证方案的挑战与不足，讨论了开放的研究问题，并提出了首个面向基于机器学习的分布式系统的认证方案。