  Federated learning (FL) aided health diagnostic models can incorporate data
from a large number of personal edge devices (e.g., mobile phones) while
keeping the data local to the originating devices, largely ensuring privacy.
However, such a cross-device FL approach for health diagnostics still imposes
many challenges due to both local data imbalance (as extreme as local data
consists of a single disease class) and global data imbalance (the disease
prevalence is generally low in a population). Since the federated server has no
access to data distribution information, it is not trivial to solve the
imbalance issue towards an unbiased model. In this paper, we propose FedLoss, a
novel cross-device FL framework for health diagnostics. Here the federated
server averages the models trained on edge devices according to the predictive
loss on the local data, rather than using only the number of samples as
weights. As the predictive loss better quantifies the data distribution at a
device, FedLoss alleviates the impact of data imbalance. Through a real-world
dataset on respiratory sound and symptom-based COVID-$19$ detection task, we
validate the superiority of FedLoss. It achieves competitive COVID-$19$
detection performance compared to a centralised model with an AUC-ROC of
$79\%$. It also outperforms the state-of-the-art FL baselines in sensitivity
and convergence speed. Our work not only demonstrates the promise of federated
COVID-$19$ detection but also paves the way to a plethora of mobile health
model development in a privacy-preserving fashion.
