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In this paper, we introduce Active Learning framework in Federated Learningfor Target Domain Generalisation, harnessing the strength from both learningparadigms. Our framework, FEDALV, composed of Active Learning (AL) andFederated Domain Generalisation (FDG), enables generalisation of an imageclassification model trained from limited source domain client's data withoutsharing images to an unseen target domain. To this end, our FDG, FEDA, consistsof two optimisation updates during training, one at the client and another atthe server level. For the client, the introduced losses aim to reduce featurecomplexity and condition alignment, while in the server, the regularisationlimits free energy biases between source and target obtained by the globalmodel. The remaining component of FEDAL is AL with variable budgets, whichqueries the server to retrieve and sample the most informative local data forthe targeted client. We performed multiple experiments on FDG w/ and w/o AL andcompared with both conventional FDG baselines and Federated Active Learningbaselines. Our extensive quantitative experiments demonstrate the superiorityof our method in accuracy and efficiency compared to the multiple contemporarymethods. FEDALV manages to obtain the performance of the full training targetaccuracy while sampling as little as 5% of the source client's data.

Federated Active Learning for Target Domain Generalisation