  In this paper, we introduce Active Learning framework in Federated Learning
for Target Domain Generalisation, harnessing the strength from both learning
paradigms. Our framework, FEDALV, composed of Active Learning (AL) and
Federated Domain Generalisation (FDG), enables generalisation of an image
classification model trained from limited source domain client's data without
sharing images to an unseen target domain. To this end, our FDG, FEDA, consists
of two optimisation updates during training, one at the client and another at
the server level. For the client, the introduced losses aim to reduce feature
complexity and condition alignment, while in the server, the regularisation
limits free energy biases between source and target obtained by the global
model. The remaining component of FEDAL is AL with variable budgets, which
queries the server to retrieve and sample the most informative local data for
the targeted client. We performed multiple experiments on FDG w/ and w/o AL and
compared with both conventional FDG baselines and Federated Active Learning
baselines. Our extensive quantitative experiments demonstrate the superiority
of our method in accuracy and efficiency compared to the multiple contemporary
methods. FEDALV manages to obtain the performance of the full training target
accuracy while sampling as little as 5% of the source client's data.
