  Widely available healthcare services are now getting popular because of
advancements in wearable sensing techniques and mobile edge computing. People's
health information is collected by edge devices such as smartphones and
wearable bands for further analysis on servers, then send back suggestions and
alerts for abnormal conditions. The recent emergence of federated learning
allows users to train private data on local devices while updating models
collaboratively. However, the heterogeneous distribution of the health
condition data may lead to significant risks to model performance due to class
imbalance. Meanwhile, as FL training is powered by sharing gradients only with
the server, training data is almost inaccessible. The conventional solutions to
class imbalance do not work for federated learning. In this work, we propose a
new federated learning framework FedImT, dedicated to addressing the challenges
of class imbalance in federated learning scenarios. FedImT contains an online
scheme that can estimate the data composition during each round of aggregation,
then introduces a self-attenuating iterative equivalent to track variations of
multiple estimations and promptly tweak the balance of the loss computing for
minority classes. Experiments demonstrate the effectiveness of FedImT in
solving the imbalance problem without extra energy consumption and avoiding
privacy risks.
