Mobility-Aware Federated Self-supervised Learning in Vehicular Network

TL;DR

This paper introduces FLSimCo, a federated self-supervised learning algorithm for vehicular networks that uses image blur levels for aggregation, enabling fast, label-free pre-training and improved model convergence in high-velocity scenarios.

Contribution

It proposes a novel federated learning algorithm based on image blur levels for aggregation, facilitating label-free pre-training in vehicular environments.

Findings

Fast and stable convergence demonstrated in simulations

Effective handling of high-velocity vehicle data

Improved model accuracy without labeled data

Abstract

Federated Learning (FL) is an advanced distributed machine learning approach, that protects the privacy of each vehicle by allowing the model to be trained on multiple devices simultaneously without the need to upload all data to a road side unit (RSU). This enables FL to handle scenarios with sensitive or widely distributed data. However, in these fields, it is well known that the labeling costs can be a significant expense, and models relying on labels are not suitable for these rapidly evolving fields especially in vehicular networks, or mobile internet of things (MIoT), where new data emerges constantly. To handle this issue, the self-supervised learning paves the way for training without labels. Additionally, for vehicles with high velocity, owing to blurred images, simple aggregation not only impacts the accuracy of the aggregated model but also reduces the convergence speed of FL. This paper proposes a FL algorithm based on image blur level to aggregation, called FLSimCo, which does not require labels and serves as a pre-training stage for self-supervised learning in the vehicular environment. Simulation results demonstrate that the proposed algorithm exhibits fast and stable convergence.