AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving

TL;DR

AutoFed is a heterogeneity-aware federated learning framework that enhances multimodal sensor data utilization for robust autonomous driving, addressing data heterogeneity and improving detection accuracy and robustness.

Contribution

It introduces a novel model with pseudo-labeling, an autoencoder-based data imputation, and a client selection mechanism to handle heterogeneity in federated multimodal learning for autonomous vehicles.

Findings

Significantly improves detection precision and recall.

Demonstrates robustness under adverse weather conditions.

Enhances training stability and convergence rate.

Abstract

Object detection with on-board sensors (e.g., lidar, radar, and camera) play a crucial role in autonomous driving (AD), and these sensors complement each other in modalities. While crowdsensing may potentially exploit these sensors (of huge quantity) to derive more comprehensive knowledge, \textit{federated learning} (FL) appears to be the necessary tool to reach this potential: it enables autonomous vehicles (AVs) to train machine learning models without explicitly sharing raw sensory data. However, the multimodal sensors introduce various data heterogeneity across distributed AVs (e.g., label quantity skews and varied modalities), posing critical challenges to effective FL. To this end, we present AutoFed as a heterogeneity-aware FL framework to fully exploit multimodal sensory data on AVs and thus enable robust AD. Specifically, we first propose a novel model leveraging pseudo-labeling to avoid mistakenly treating unlabeled objects as the background. We also propose an autoencoder-based data imputation method to fill missing data modality (of certain AVs) with the available ones. To further reconcile the heterogeneity, we finally present a client selection mechanism exploiting the similarities among client models to improve both training stability and convergence rate. Our experiments on benchmark dataset confirm that AutoFed substantially improves over status quo approaches in both precision and recall, while demonstrating strong robustness to adverse weather conditions.