Decentralised simulations¶
Peer-to-peer decentralised learning simulation.
Each honest worker holds its own model. Each round runs two phases:
Local optimisation — each worker computes a gradient on its local batch and updates its own model.
Model mixing — each worker gathers models from other nodes and replaces its model with an aggregate of the received set.
Two abstract seams let protocols vary the local update rule (e.g. momentum-SGD) and the communication topology (which models each worker receives).