  Various health-care applications such as assisted living, fall detection,
etc., require modeling of user behavior through Human Activity Recognition
(HAR). Such applications demand characterization of insights from multiple
resource-constrained user devices using machine learning techniques for
effective personalized activity monitoring. On-device Federated Learning proves
to be an effective approach for distributed and collaborative machine learning.
However, there are a variety of challenges in addressing statistical (non-IID
data) and model heterogeneities across users. In addition, in this paper, we
explore a new challenge of interest -- to handle heterogeneities in labels
(activities) across users during federated learning. To this end, we propose a
framework for federated label-based aggregation, which leverages overlapping
information gain across activities using Model Distillation Update. We also
propose that federated transfer of model scores is sufficient rather than model
weight transfer from device to server. Empirical evaluation with the
Heterogeneity Human Activity Recognition (HHAR) dataset (with four activities
for effective elucidation of results) on Raspberry Pi 2 indicates an average
deterministic accuracy increase of at least ~11.01%, thus demonstrating the
on-device capabilities of our proposed framework.
