"This workshop focuses on the challenges involved in building integrated scalable distributed systems for the healthcare analytics domains. Healthcare analytics offers a unique opportunity to explore scalable system design since there has been a tectonic shift in the ability of medical institutions to capture and store unprecedented amount of structured and unstructured medical data, including the new ability to stream unstructured medical data in real time. This shift has already contributed to an ecosystem of Machine Learning (ML) models being trained for a variety of clinical tasks. However, new approaches are required to build systems that can develop and deploy ML models based on distributed healthcare data that must necessarily be accessed with privacy-preserving constraints.

The goal of this workshop is to attract leading researchers to share and discuss their latest results involving approaches to building scalable platforms for privacy-aware collaborative learning and inference that can be applicable to the domain of healthcare analytics. The scope of the workshop includes (but is not limited to) the following challenges:
* Scalable and distributed learning
* Continuous federated learning with privacy constraints
* Enforcing soft real-time constraints for streaming data analytics
* Specialized heterogeneous hardware for learning and inference
* Scalable runtime and resource allocation systems
* Productive systems for developing scalable data analytics applications"