Authors: Shreya Gummadi, Mateus V. Gasparino, Deepak Vasisht, Girish Chowdhary
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.
Source: http://arxiv.org/abs/2411.04112v1
