Privacy Enhancing and Scalable Federated Learning to Accelerate AI Implementation in Cross-Silo and IoMT Environments

In FL, the model is trained and shared across decentralized locations where data are privately owned. After local training, model updates are sent back to a central server, thus enabling access to distributed data on a large scale while maintaining privacy, security, and data access rights.

Although FL is a well-studied topic, existing frameworks are still at an early stage of development. They encounter challenges with respect to scalability, data security, aggregation methodologies, data provenance, and production readiness. In this paper, we propose a novel FL framework that supports functionalities like scalable processing with respect of data, devices, sites and collaborators, monitoring services, privacy, and support for use cases.