  Federated learning is an effective way of extracting insights from different
user devices while preserving the privacy of users. However, new classes with
completely unseen data distributions can stream across any device in a
federated learning setting, whose data cannot be accessed by the global server
or other users. To this end, we propose a unified zero-shot framework to handle
these aforementioned challenges during federated learning. We simulate two
scenarios here -- 1) when the new class labels are not reported by the user,
the traditional FL setting is used; 2) when new class labels are reported by
the user, we synthesize Anonymized Data Impressions by calculating class
similarity matrices corresponding to each device's new classes followed by
unsupervised clustering to distinguish between new classes across different
users. Moreover, our proposed framework can also handle statistical
heterogeneities in both labels and models across the participating users. We
empirically evaluate our framework on-device across different communication
rounds (FL iterations) with new classes in both local and global updates, along
with heterogeneous labels and models, on two widely used audio classification
applications -- keyword spotting and urban sound classification, and observe an
average deterministic accuracy increase of ~4.041% and ~4.258% respectively.
