Federated learning isn’t just about moving models instead of data. It’s about trust. 🤝

We talk a lot about the technical side—keeping data local, optimizing communication rounds, handling non-IID distributions. But in healthcare, those are solvable problems.

The hard part? Convincing hospitals, researchers, and regulators that collaboration is actually safe, fair, and worthwhile.

In practice, most barriers are organizational, not technical:

• Who owns the model when five institutions contributed data?
• Who’s liable when it fails in a clinical setting?
• How do we ensure reproducibility when the data never leave the site?
• How do we verify fairness across institutions with different patient populations?

I’m increasingly convinced that the next breakthroughs in federated learning won’t come from better algorithms—they’ll come from frameworks for governance, transparency, and accountability that make collaboration actually work in practice.

The technical foundations are maturing. The institutional infrastructure? That’s where the real work begins.

One of the biggest takeaways from FLTA2025: the conversations about deployment barriers were more valuable than any algorithm discussion. 📍 Dubrovnik

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