  Conventional centralised deep learning paradigms are not feasible when data
from different sources cannot be shared due to data privacy or transmission
limitation. To resolve this problem, federated learning has been introduced to
transfer knowledge across multiple sources (clients) with non-shared data while
optimising a globally generalised central model (server). Existing federated
learning paradigms mostly focus on transferring holistic high-level knowledge
(such as class) across models, which are closely related to specific objects of
interest so may suffer from inverse attack. In contrast, in this work, we
consider transferring mid-level semantic knowledge (such as attribute) which is
not sensitive to specific objects of interest and therefore is more
privacy-preserving and scalable. To this end, we formulate a new Federated
Zero-Shot Learning (FZSL) paradigm to learn mid-level semantic knowledge at
multiple local clients with non-shared local data and cumulatively aggregate a
globally generalised central model for deployment. To improve model
discriminative ability, we propose to explore semantic knowledge augmentation
from external knowledge for enriching the mid-level semantic space in FZSL.
Extensive experiments on five zeroshot learning benchmark datasets validate the
effectiveness of our approach for optimising a generalisable federated learning
model with mid-level semantic knowledge transfer.
