We propose how a developing country like Sri Lanka can benefit from privacy-enabled machine learning techniques such as Federated Learning to detect road conditions using crowd-sourced data collection and proposed the idea of implementing a Digital Twin for the national road system in Sri Lanka. Developing countries such as Sri Lanka are far behind in implementing smart road systems and smart cities compared to the developed countries. The proposed work discussed in this paper matches the UN Sustainable Development Goal (SDG) 9: "Build Resilient Infrastructure, Promote Inclusive and Sustainable Industrialization and Foster Innovation". Our proposed work discusses how the government and private sector vehicles that conduct routine trips to collect crowd-sourced data using smartphone devices to identify the road conditions and detect where the potholes, surface unevenness (roughness), and other major distresses are located on the roads. We explore Mobile Edge Computing (MEC) techniques that can bring machine learning intelligence closer to the edge devices where produced data is stored and show how the applications of Federated Learning can be made to detect and improve road conditions. During the second phase of this study, we plan to implement a Digital Twin for the road system in Sri Lanka. We intend to use data provided by both Dedicated and Non-Dedicated systems in the proposed Digital Twin for the road system. As of writing this paper, and best to our knowledge, there is no Digital Twin system implemented for roads and other infrastructure systems in Sri Lanka. The proposed Digital Twin will be one of the first implementations of such systems in Sri Lanka. Lessons learned from this pilot project will benefit other developing countries who wish to follow the same path and make data-driven decisions.

本文提出了使用联邦学习等隐私启用机器学习技术来检测路况，并在斯里兰卡提出实施国家道路系统的数字孪生的想法，以促进联合国可持续发展目标9的实现，“建设具有弹性的基础设施，促进包容性和可持续的产业化和创新”。

Sensing and Mapping for Better Roads: Initial Plan for Using Federated
  Learning and Implementing a Digital Twin to Identify the Road Conditions in a
  Developing Country -- Sri Lanka

更好的道路感知和制图：使用联邦学习和数字双胞胎初步规划，在开发中国家（斯里兰卡）中识别道路条件