Abstract:

The analysis of healthcare data is increasingly challenging due to the exponential growth in data volume and the diversity of data formats available. This is compounded by the necessity for patient confidentiality and security. Traditional healthcare systems, often localized, face significant barriers to the goal of achieving efficient information use that limits the use of advanced machine learning. We examine methodologies for deriving a global machine learning (ML) model. We examine the role of federated learning (FL) and swarm learning (SL) in enhancing privacy, security, and interoperability in healthcare data and model management for the development of scalable, collaborative model training. Utilizing cloud computing and Internet of Things (IoT) technologies as the ecosystem for machine learning can result in comprehensively addressing current limitations in healthcare informatics. We present an architecture for the development of a global model that incorporates the FL and SL incorporating a Swarm Intelligence communication layer.