Federated Learning (FL) allows for collaboratively aggregating learned information across several computing devices and sharing the same amongst them, thereby tackling issues of privacy and the need of huge bandwidth. FL techniques generally use a central server or cloud for aggregating the models received from the devices. Such centralized FL techniques suffer from inherent problems such as failure of the central node and bottlenecks in channel bandwidth. When FL is used in conjunction with connected robots serving as devices, a failure of the central controlling entity can lead to a chaotic situation. This paper describes a mobile agent based paradigm to decentralize FL in multi-robot scenarios. Using Webots, a popular free open-source robot simulator, and Tartarus, a mobile agent platform, we present a methodology to decentralize federated learning in a set of connected robots. With Webots running on different connected computing systems, we show how mobile agents can perform the task of Decentralized Federated Reinforcement Learning (dFRL). Results obtained from experiments carried out using Q-learning and SARSA by aggregating their corresponding Q-tables, show the viability of using decentralized FL in the domain of robotics. Since the proposed work can be used in conjunction with other learning algorithms and also real robots, it can act as a vital tool for the study of decentralized FL using heterogeneous learning algorithms concurrently in multi-robot scenarios.
翻译:联邦学习联合会(FL)允许通过合作汇集在若干计算设备中学习到的信息,并在它们之间分享同样的信息,从而解决隐私问题和巨大的带宽需求。FL技术通常使用中央服务器或云层来汇总从这些设备收到的模型。这种中央FL技术存在内在问题,如中央节点的故障和频道带宽的瓶颈。当FL与作为设备的连接机器人一起使用FL时,中央控制实体的故障可能导致混乱局面。本文描述了一种基于移动代理的范式,在多机器人情景中分散使用FL。使用流行的免费开放源机器人模拟器(webots)和Tartarus(移动代理平台)通常使用中央服务器或云来汇总从这些设备收到的模型。这种集中的FL技术会遇到内在问题,例如中央节点的故障和频道带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带宽带