Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigation

TL;DR

Fed-EC introduces a clustering-based federated learning framework for autonomous robot navigation that significantly reduces communication costs and improves model adaptation across diverse outdoor environments.

Contribution

The paper presents Fed-EC, a novel clustering-based federated learning approach tailored for vision-based autonomous robots in diverse outdoor settings, addressing non-IID data challenges.

Findings

Reduces communication size by 23x per robot.

Matches centralized learning performance in goal-oriented navigation.

Outperforms local learning methods.

Abstract

Centralized learning requires data to be aggregated at a central server, which poses significant challenges in terms of data privacy and bandwidth consumption. Federated learning presents a compelling alternative, however, vanilla federated learning methods deployed in robotics aim to learn a single global model across robots that works ideally for all. But in practice one model may not be well suited for robots deployed in various environments. This paper proposes Federated-EmbedCluster (Fed-EC), a clustering-based federated learning framework that is deployed with vision based autonomous robot navigation in diverse outdoor environments. The framework addresses the key federated learning challenge of deteriorating model performance of a single global model due to the presence of non-IID data across real-world robots. Extensive real-world experiments validate that Fed-EC reduces the communication size by 23x for each robot while matching the performance of centralized learning for goal-oriented navigation and outperforms local learning. Fed-EC can transfer previously learnt models to new robots that join the cluster.