Social media platforms such as Twitter provide an excellent resource for mobile communication during emergency events. During the sudden onset of a natural or artificial disaster, important information may be posted on Twitter or similar web forums. This information can be used for disaster response and crisis management if processed accurately. However, the data present in such situations is ever-changing, and considerable resources during such crisis may not be readily available. Therefore, a low resource, continually learning system must be developed to incorporate and make NLP models robust against noisy and unordered data. We utilise regularisation to alleviate catastrophic forgetting in the target neural networks while taking a distributed approach to enable learning on resource-constrained devices. We employ federated learning for distributed learning and aggregation of the central model for continual deployment.

翻译：诸如Twitter等社交媒体平台为紧急事件期间的移动通信提供了极好的资源。在自然或人为灾害突发时,重要信息可以张贴在Twitter或类似的网络论坛上。如果处理准确,这些信息可用于救灾和危机管理。然而,这类情况下的数据不断变化,危机期间的大量资源可能无法随时获得。因此,必须开发一个低资源、持续学习的系统,以吸收和强化NLP模型,抵御吵闹和无序的数据。我们利用常规化来减轻目标神经网络中的灾难性遗忘,同时采取分布式方法,以便能够学习受资源限制的装置。我们利用联合学习来传播学习和汇总中央模型,以便持续部署。