Federated Learning, a new machine learning paradigm enhancing the use of edge devices, is receiving a lot of attention in the pervasive community to support the development of smart services. Nevertheless, this approach still needs to be adapted to the specificity of the pervasive domain. In particular, issues related to continual learning need to be addressed. In this paper, we present a distillation-based approach dealing with catastrophic forgetting in federated learning scenario. Specifically, Human Activity Recognition tasks are used as a demonstration domain.
翻译:联邦学习组织是加强边缘设备使用的一种新的机器学习模式,在广大社区受到许多关注,以支持智能服务的发展,然而,这一方法仍然需要适应普遍领域的特殊性,特别是需要解决与持续学习有关的问题。在本文件中,我们提出了一个基于蒸馏的方法,处理联盟学习情景中的灾难性遗忘。具体地说,人类活动识别任务被用作示范领域。