  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.
