  Despite significant improvements over the last few years, cloud-based
healthcare applications continue to suffer from poor adoption due to their
limitations in meeting stringent security, privacy, and quality of service
requirements (such as low latency). The edge computing trend, along with
techniques for distributed machine learning such as federated learning, have
gained popularity as a viable solution in such settings. In this paper, we
leverage the capabilities of edge computing in medicine by analyzing and
evaluating the potential of intelligent processing of clinical visual data at
the edge allowing the remote healthcare centers, lacking advanced diagnostic
facilities, to benefit from the multi-modal data securely. To this aim, we
utilize the emerging concept of clustered federated learning (CFL) for an
automatic diagnosis of COVID-19. Such an automated system can help reduce the
burden on healthcare systems across the world that has been under a lot of
stress since the COVID-19 pandemic emerged in late 2019. We evaluate the
performance of the proposed framework under different experimental setups on
two benchmark datasets. Promising results are obtained on both datasets
resulting in comparable results against the central baseline where the
specialized models (i.e., each on a specific type of COVID-19 imagery) are
trained with central data, and improvements of 16\% and 11\% in overall
F1-Scores have been achieved over the multi-modal model trained in the
conventional Federated Learning setup on X-ray and Ultrasound datasets,
respectively. We also discuss in detail the associated challenges,
technologies, tools, and techniques available for deploying ML at the edge in
such privacy and delay-sensitive applications.
