Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning

arXiv:2211.14769v4 Announce Type: replace-cross
Abstract: Federated embodied agent learning protects the data privacy of individual visual environments by keeping data locally at each client (the individual environment) during training. However, since the local data is inaccessible to the server under federated learning, attackers may easily poison the training data of the local client to build a backdoor in the agent without notice. Deploying such an agent raises the risk of potential harm to humans, as the attackers may easily navigate …

agent arxiv attackers building client cs.ai cs.cl cs.cr cs.cv data data privacy environment environments federated federated learning local locally may navigation privacy server training under