A scoping review of the governance of federated learning in healthcare

A scoping review of the governance of federated learning in healthcare

In healthcare, federated learning (FL) is emerging as a methodology to enable the analysis of large and disparate datasets while allowing custodians to retain sovereignty. While FL minimises data-sharing challenges, concerns surrounding ethics, privacy, maleficent use, and harm remain. These concerns can be managed by effective data governance. Data governance specifies procedural, relational, and structural mechanisms governing how data is capt…

Federated Learning vs Centralized Data: What’s the Path to More Private and Efficient AI?

For decades, the development of Artificial Intelligence (AI) has been closely tied to the mass collection of data in centralized servers. This approach has enabled major advances—from prediction models to virtual assistants and recommendation systems. However, as the sensitivity of data has increased—especially in sectors like healthcare, finance, or defense—so too have the risks associated with centralization.In response to this traditional par…