  The Federated Learning setting has a central server coordinating the training
of a model on a network of devices. One of the challenges is variable training
performance when the dataset has a class imbalance. In this paper, we address
this by introducing a new loss function called Fed-Focal Loss. We propose to
address the class imbalance by reshaping cross-entropy loss such that it
down-weights the loss assigned to well-classified examples along the lines of
focal loss. Additionally, by leveraging a tunable sampling framework, we take
into account selective client model contributions on the central server to
further focus the detector during training and hence improve its robustness.
Using a detailed experimental analysis with the VIRTUAL (Variational Federated
Multi-Task Learning) approach, we demonstrate consistently superior performance
in both the balanced and unbalanced scenarios for MNIST, FEMNIST, VSN and HAR
benchmarks. We obtain a more than 9% (absolute percentage) improvement in the
unbalanced MNIST benchmark. We further show that our technique can be adopted
across multiple Federated Learning algorithms to get improvements.
