RESEARCH

BytesAndBrains: A Shared Runtime for Machine Learning Outside the Data Center

Federated learning, gossip learning, split learning, and peer-to-peer inference each ship with their own networking stack and training loop. This position paper describes a runtime layer they can share instead: computation authored as a partitionable graph, bytes managed between nodes, and every distributed strategy treated as a placement and binding problem on the same surface.