Trainers do not upload datasets to the network. Training runs locally on the trainer’s own machine using their hardware and data. After training is complete, they submit only gradients. Raw data is never shared, stored, or inspected by anyone else. Privacy is preserved by the system design rather than by rules or trust. This allows trainers to contribute compute and improve models without exposing sensitive or proprietary information.