Gossip learning removes the aggregation server entirely: nodes exchange models with random peers and merge what arrives. It remained competitive with federated learning across the tested churn and communication scenarios. Where Federated Averaging leaves coordination centralized, this result gives Open Epistemic Intelligence a peer-to-peer propagation mechanism and a concrete decentralization contrast, without extending the evidence to arbitrary network failures.
Gossip learning removes the server. Nodes hold models, pass them to random peers, and merge what arrives. There is nothing in the middle to trust or to fail.
The paper compares it against federated learning head to head, over the same tasks and the same churn and message-loss conditions.
Gossip learning is competitive. The result matters because the server in federated learning is usually justified on performance grounds, and here that justification does not hold.