A Dispersed Federated Learning Framework for 6G-Enabled Autonomous Driving Cars

TL;DR

This paper introduces a dispersed federated learning framework tailored for 6G-enabled autonomous vehicles, focusing on robustness, privacy, and communication efficiency, and proposes an optimization solution validated through extensive simulations.

Contribution

It presents a novel dispersed federated learning framework with an optimization approach to enhance robustness and privacy in autonomous driving applications.

Findings

The proposed BSUM-based scheme outperforms baseline methods.

The framework effectively reduces communication resource consumption.

Numerical results confirm improved model accuracy and privacy protection.

Abstract

Sixth-Generation (6G)-based Internet of Everything applications (e.g. autonomous driving cars) have witnessed a remarkable interest. Autonomous driving cars using federated learning (FL) has the ability to enable different smart services. Although FL implements distributed machine learning model training without the requirement to move the data of devices to a centralized server, it its own implementation challenges such as robustness, centralized server security, communication resources constraints, and privacy leakage due to the capability of a malicious aggregation server to infer sensitive information of end-devices. To address the aforementioned limitations, a dispersed federated learning (DFL) framework for autonomous driving cars is proposed to offer robust, communication resource-efficient, and privacy-aware learning. A mixed-integer non-linear (MINLP) optimization problem is formulated to jointly minimize the loss in federated learning model accuracy due to packet errors and transmission latency. Due to the NP-hard and non-convex nature of the formulated MINLP problem, we propose the Block Successive Upper-bound Minimization (BSUM) based solution. Furthermore, the performance comparison of the proposed scheme with three baseline schemes has been carried out. Extensive numerical results are provided to show the validity of the proposed BSUM-based scheme.