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A Concentration Bound for Distributed Stochastic Approximation

arXiv 2210.04253 ↗First posted 9 Oct 2022stat.ML cs.LG math.PRcited by 0

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Abstract (arXiv)

We revisit the classical model of Tsitsiklis, Bertsekas and Athans for distributed stochastic approximation with consensus. The main result is an analysis of this scheme using the ODE approach to stochastic approximation, leading to a high probability bound for the tracking error between suitably interpolated iterates and the limiting differential equation. Several future directions will also be highlighted.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 9 references that are in the map. First posted on arXiv 9 Oct 2022.

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3 of 9 references.

References · 3

  1. 2021Concentration of Contractive Stochastic Approximation and Reinforcement Learningcited by 23
  2. 2015A Concentration Bound for Stochastic Approximation via Alekseev's Formulacited by 16
  3. 2017Distributed Stochastic Approximation with Local Projectionscited by 1

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