Adaptive Segmentation Enhanced Asynchronous Federated Learning for Sustainable Intelligent Transportation Systems

The proliferation of advanced embedded and communication technologies has facilitated the possibility of modern Intelligent Transportation System (ITS). The hierarchical nature of such large-scale and distributed systems brings obvious challenges in creating a scalable and sustainable computing environment, and hence the development and application of edge intelligence become critical. Federated learning (FL), as an emerging distributed machine learning paradigm, aims to offer secure knowledge sharing and effective learning across multiple devices. However, conventional FL may fall into trouble when facing large-scale and network-agnostic systems with fast moving devices and changing network attributes. In this study, we propose an Adaptive Segmentation enhanced Asynchronous Federated Learning (AS-AFL) model, aiming to improve the learning efficiency and reliability in sustainable ITS via a decentralized fashion. Specifically, a meta-learning based adaptive segmentation scheme is designed to automatically separate the client nodes (e.g., vehicles) into multiple edge groups according to their homogeneous attributes. An integrated aggregation mechanism is then developed to realize the horizontal FL among a group of similar client nodes via the so-called intra-group synchronous aggregation, while allowing the vertical FL across different groups via the so-called inter-group asynchronous aggregation. Experiment and evaluation results based on an open-source dataset demonstrate the outstanding learning and communication performance of our proposed model, compared with several conventional FL schemes in a distributed ITS application scenario.

I. Introduction

The wide penetration and deployment of communication infrastructures and embedded devices are moving the future Internet of Things (IoT) technologies beyond personal applications. Geographically distributed and hierarchical IoT systems for applications, such as smart cities/nations, pervasive healthcare, and Intelligent Transportation Systems (ITS), are soon becoming the reality [1], [2], [3], [4]. Large amounts of data are expected to be generated by billions of interconnected devices that are communicating via different networks and different protocols [5]. The hierarchical nature of such large-scale systems brings obvious challenges in managing scalability and sustainability. Edge intelligence is becoming a critical aspect to ensure scalable and reliable distributed systems in future sustainable ITS, which calls for new distributed machine learning techniques to effectively manage and leverage the large amounts of data collected over heterogeneous devices and networks across hierarchical topologies [6].