  Federated Learning enables training of a general model through edge devices
without sending raw data to the cloud. Hence, this approach is attractive for
digital health applications, where data is sourced through edge devices and
users care about privacy. Here, we report on the feasibility to train deep
neural networks on the Raspberry Pi4s as edge devices. A CNN, a LSTM and a MLP
were successfully trained on the MNIST data-set. Further, federated learning is
demonstrated experimentally on IID and non-IID samples in a parametric study,
to benchmark the model convergence. The weight updates from the workers are
shared with the cloud to train the general model through federated learning.
With the CNN and the non-IID samples a test-accuracy of up to 85% could be
achieved within a training time of 2 minutes, while exchanging less than $10$
MB data per device. In addition, we discuss federated learning from an use-case
standpoint, elaborating on privacy risks and labeling requirements for the
application of emotion detection from sound. Based on the experimental
findings, we discuss possible research directions to improve model and system
performance. Finally, we provide best practices for a practitioner, considering
the implementation of federated learning.
