Cordial Learning: Distributed Training with Correlated Data

The paper introduces cordial learning, a distributed training framework for agents with correlated data, where each agent’s label depends on inputs from other agents. By sharing only low‑dimensional outputs, agents train local models that extract informative signals from peers, forming a game‑theoretic loss that depends on others’ models. The authors prove convergence to a globally optimal solution for linear models and show empirical effectiveness on structured multi‑digit MNIST tasks, even in nonlinear settings.

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