Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles

TL;DR

This paper introduces C-DFL, a decentralized federated learning framework for connected vehicles that enhances scalability and robustness by eliminating reliance on a central server and improving data aggregation.

Contribution

The paper proposes a novel decentralized federated learning framework, C-DFL, tailored for connected vehicles, addressing limitations of centralized approaches and reducing data redundancy issues.

Findings

C-DFL outperforms traditional federated learning methods in simulations.

Decentralized approach improves scalability and fault tolerance.

Enhanced learning quality in V2X networks.

Abstract

Advanced researches on connected vehicles have recently targeted to the integration of vehicle-to-everything (V2X) networks with Machine Learning (ML) tools and distributed decision making. Federated learning (FL) is emerging as a new paradigm to train machine learning (ML) models in distributed systems, including vehicles in V2X networks. Rather than sharing and uploading the training data to the server, the updating of model parameters (e.g., neural networks' weights and biases) is applied by large populations of interconnected vehicles, acting as local learners. Despite these benefits, the limitation of existing approaches is the centralized optimization which relies on a server for aggregation and fusion of local parameters, leading to the drawback of a single point of failure and scaling issues for increasing V2X network size. Meanwhile, in intelligent transport scenarios, data collected from onboard sensors are redundant, which degrades the performance of aggregation. To tackle these problems, we explore a novel idea of decentralized data processing and introduce a federated learning framework for in-network vehicles, C-DFL(Consensus based Decentralized Federated Learning), to tackle federated learning on connected vehicles and improve learning quality. Extensive simulations have been implemented to evaluate the performance of C-DFL, that demonstrates C-DFL outperforms the performance of conventional methods in all cases.