SmartData Fusion allows digital service providers to connect fragmented and limited data sources into a cohesive learning ecosystem using federated learning.

This means they can train models across multiple, decentralized datasets without the need to centralize data.

The platform also employs semi-supervised learning techniques that utilize small amounts of labeled data to extrapolate and infer patterns from larger datasets of unlabeled data.

This hybrid approach not only maximizes the utility of available data but also preserves data privacy and security, as sensitive information does not need to be transferred or exposed.

The combination of federated and semi-supervised learning techniques is gaining traction with increasing computational power and machine learning advances, making this solution technologically feasible.

The primary cost barrier lies in developing the sophisticated algorithms and infrastructure to support decentralized learning.

Regulatory issues may revolve around data privacy laws, but by design, federated learning aligns with many of these requirements.