# Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms

arXiv: https://arxiv.org/abs/2006.13460 · HTML: https://mlsanity.com/paper/2006.13460/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [Federated Learning under Heterogeneous and Correlated Client Availability](https://mlsanity.com/paper/2301.04632/) (2023)
- [SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning](https://mlsanity.com/paper/2402.04114/) (2024)
- [The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication](https://mlsanity.com/paper/2103.13026/) (2021)
- [Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation](https://mlsanity.com/paper/2605.19629/) (2026)

## Abstract (arXiv)

Motivated by broad applications in reinforcement learning and federated learning, we study local stochastic approximation over a network of agents, where their goal is to find the root of an operator composed of the local operators at the agents. Our focus is to characterize the finite-time performance of this method when the data at each agent are generated from Markov processes, and hence they are dependent. In particular, we provide the convergence rates of local stochastic approximation for both constant and time-varying step sizes. Our results show that these rates are within a logarithmic factor of the ones under independent data. We then illustrate the applications of these results to different interesting problems in multi-task reinforcement learning and federated learning.