My company runs across different markets and different continents. We are currently merging our data science functions across those markets, but, as expected there is pushback. To start the ball rolling with collaboration amongst the teams, i have suggested that we build a federated learning model. This way no one would need to share data and just model weights. Has anyone had experience implementing this king of DS model?

In my experience with global data science integrations, implementing federated learning frameworks has proven highly effective for enabling collaboration across geographically- and functionally-diverse teams without the need for direct data sharing. This approach aligns with best practices in regulated and multi-market environments, supporting both top-down strategy and bottom-up innovation while addressing policy, risk, and cultural challenges.

For these initiatives to succeed, robust metadata management is essential. Metadata about data origin, schema, quality, governance, and lineage ensures models can be reliably trained, aggregated, and interpreted across markets. A strong metadata foundation also improves model transparency, regulatory compliance, and downstream trust in enterprise-wide data science applications.

Adopting federated learning—along with comprehensive metadata standards—provides a scalable, secure framework for unifying analytics and delivering tangible business value across the enterprise.

Our approach was to bring the teams together in a Data Science Community to start with, focused on monthly learning and sharing. We then brought in our Data Framework and Data Governance as central governance parts that we also pushed into the Data Science Community to bring transparency over the data sets in our Data Catalogue. We also launched a Data Festival (mini-annual conference) and a Data Science Hack challenge both of which are run out of our Data Culture working group.

I am not sure I see any place for pushback on companies data assets but I appreciate other industries have challenges around regulatory boundaries. Overall we seek federation not centralization of our Data Scientists - however the data governance and tooling is centralized.

We faced a similar challenge while merging data science teams across global upstream operations exploration, drilling, and production. A federated learning model was the right bridge between collaboration and compliance.

Each market trained models locally on Azure Databricks using its own subsurface and production data, while only encrypted model weights were shared centrally through Azure ML. This preserved data privacy and regulatory boundaries.

We started with use cases like predictive maintenance and drilling efficiency, which showed quick ROI and reduced skepticism. Establishing a small, federated data council helped align on governance, metrics, and retraining cadence.

Federated learning works best when combined with clear ownership, lightweight orchestration, and early demonstration of business impact. It not only improves accuracy but also builds trust and collaboration across teams.