  The growing need for technology that supports remote healthcare is being
acutely highlighted by an aging population and the COVID-19 pandemic. In
health-related machine learning applications the ability to learn predictive
models without data leaving a private device is attractive, especially when
these data might contain features (e.g., photographs or videos of the body)
that make identifying a subject trivial and/or the training data volume is
large (e.g., uncompressed video). Camera-based remote physiological sensing
facilitates scalable and low-cost measurement, but is a prime example of a task
that involves analysing high bit-rate videos containing identifiable images and
sensitive health information. Federated learning enables privacy-preserving
decentralized training which has several properties beneficial for camera-based
sensing. We develop the first mobile federated learning camera-based sensing
system and show that it can perform competitively with traditional
state-of-the-art supervised approaches. However, in the presence of corrupted
data (e.g., video or label noise) from a few devices the performance of weight
averaging quickly degrades. To address this, we leverage knowledge about the
expected noise profile within the video to intelligently adjust how the model
weights are averaged on the server. Our results show that this significantly
improves upon the robustness of models even when the signal-to-noise ratio is
low
