  Social media platforms such as Twitter, Facebook etc can be utilised as an
important source of information during disaster events. This information can be
used for disaster response and crisis management if processed accurately and
quickly. However, the data present in such situations is ever-changing, and
using considerable resources during such a crisis is not feasible. Therefore,
we have to develop a low resource and continually learning system that
incorporates text classification models which are robust against noisy and
unordered data. We utilised Distributed learning which enabled us to learn on
resource-constrained devices, then to alleviate catastrophic forgetting in our
target neural networks we utilized regularization. We then applied federated
averaging for distributed learning and to aggregate the central model for
continual learning.
