Synchronous updates refer to a method of updating model parameters in a distributed training environment, where all nodes compute gradients and apply updates at the same time, ensuring that every participant has the same view of the model at each iteration. This approach helps maintain consistency across multiple devices, making it easier to converge to an optimal solution while minimizing communication overhead.
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