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.

翻译：集中式学习要求将数据汇聚到中央服务器，这在数据隐私和带宽消耗方面带来了重大挑战。联邦学习提供了一种引人注目的替代方案，然而，应用于机器人领域的传统联邦学习方法旨在学习一个适用于所有机器人的单一全局模型。但在实践中，一个模型可能并不完全适合部署在各种环境中的机器人。本文提出了联邦嵌入聚类（Fed-EC），这是一个基于聚类的联邦学习框架，用于部署在多样化户外环境中基于视觉的自主机器人导航。该框架解决了联邦学习中的一个关键挑战：由于现实世界机器人间存在非独立同分布数据，单一全局模型的性能会下降。大量的真实世界实验验证表明，Fed-EC将每个机器人的通信量减少了23倍，同时在面向目标的导航任务上达到了与集中式学习相当的性能，并优于局部学习。Fed-EC能够将先前学习到的模型迁移到新加入集群的机器人上。