This model analyzes convergence in distributed algorithms, indicating performance similar to centralized methods.
We present a model for asynchronous distributed computation and then proceed to analyze the convergence of natural asynchronous distributed versions of a large class of deterministic and stochastic gradient-like algorithms. We show that such algorithms retain the desirable convergence properties of their centralized counterparts, provided that the time between consecutive interprocessor communications and the communication delays are not too large.
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Tsitsiklis et al. (1986) studied this question.
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