让机器人在不同环境里各自学模型，通信量减少23倍却仍保持高效导航。
Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigation
- 按机器人视觉特征聚类，分组训练本地模型而非统一一个全局模型。
- 实测通信量降低23倍，导航性能接近集中式学习，优于单机本地学习。
- 支持新加入机器人快速迁移已有模型，适合多环境部署的自主导航系统。
集中式学习需将数据汇聚至中心服务器，面临数据隐私和带宽消耗的挑战。联邦学习虽是可行替代方案，但传统方法在机器人领域通常训练单一全局模型以适应所有机器人，而实际中不同环境下的机器人数据分布差异大，导致模型性能下降。本文提出基于聚类的联邦学习框架Fed-EC，用于多样户外环境下的视觉自主机器人导航。该框架通过聚类方式解决因非独立同分布（non-IID）数据导致的全局模型性能退化问题。大量真实世界实验表明，Fed-EC使每台机器人的通信开销减少23倍，同时在目标导向导航任务中性能媲美集中式学习，并优于本地学习。此外，该框架可将已学模型迁移到新加入集群的机器人上。
原文摘要 · Abstract (English)
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.
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