  Standard machine learning approaches require centralizing the users' data in
one computer or a shared database, which raises data privacy and
confidentiality concerns. Therefore, limiting central access is important,
especially in healthcare settings, where data regulations are strict. A
potential approach to tackling this is Federated Learning (FL), which enables
multiple parties to collaboratively learn a shared prediction model by using
parameters of locally trained models while keeping raw training data locally.
In the context of AI-assisted pain-monitoring, we wish to enable
confidentiality-preserving and unobtrusive pain estimation for long-term
pain-monitoring and reduce the burden on the nursing staff who perform frequent
routine check-ups. To this end, we propose a novel Personalized Federated Deep
Learning (PFDL) approach for pain estimation from face images. PFDL performs
collaborative training of a deep model, implemented using a lightweight CNN
architecture, across different clients (i.e., subjects) without sharing their
face images. Instead of sharing all parameters of the model, as in standard FL,
PFDL retains the last layer locally (used to personalize the pain estimates).
This (i) adds another layer of data confidentiality, making it difficult for an
adversary to infer pain levels of the target subject, while (ii) personalizing
the pain estimation to each subject through local parameter tuning. We show
using a publicly available dataset of face videos of pain (UNBC-McMaster
Shoulder Pain Database), that PFDL performs comparably or better than the
standard centralized and FL algorithms, while further enhancing data privacy.
This, has the potential to improve traditional pain monitoring by making it
more secure, computationally efficient, and scalable to a large number of
individuals (e.g., for in-home pain monitoring), providing timely and
unobtrusive pain measurement.
