Training frontier foundation models today demands massive, co-located clusters of high-end GPUs - accessible only to a handful of the most well-resourced organizations. Protocol Learning removes this co-location requirement, enabling multi-participant training of foundation models across open, permissionless networks of globally distributed compute, where no single participant has, or can ever obtain, a full copy of the model.

This requires solving hard open problems in low-bandwidth model parallelism, asynchronous distributed optimization, supporting heterogeneous hardware, fault-tolerant training systems, Byzantine robustness, and trustless verification. This workshop convenes the researchers advancing these building blocks to define the challenges ahead and chart a research roadmap for training the next generation of community-owned frontier models with self-sustaining economics.