Protocol Learning: ICML Social

​As frontier AI training concentrates inside a small number of increasingly vertically integrated labs and hyperscalers, the research community faces a structural question: is large-scale model training necessarily centralized, or is centralization an artifact of current engineering and economic choices? Recent work on distributed training, low-bandwidth communication, federated and swarm learning, suggests the latter — that meaningful decentralization is becoming technically tractable at the frontier scale, not just at the toy scale.

​This social convenes researchers and practitioners working at the intersection of decentralized training, federated learning, distributed systems and AI governance to discuss:

(1) the current technical frontier of decentralized model training,

(2) the open research problems that gate further progress — communication efficiency, verifiable computation, attribution, and adversarial robustness — and

(3) what collective or distributed ownership of frontier models could mean in practice, and why it matters as a counterweight to concentration of compute, capital, and decision-making in AI.

Join us!