Cover Image

Federated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Khodadadian, Sajad, Sharma, Pranay, Joshi, Gauri, Maguluri, Siva Theja
Format:	Preprint
Published:	2022
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Tight Finite Time Bounds of Two-Time-Scale Linear Stochastic Approximation with Markovian Noise
by: Haque, Shaan Ul, et al.
Published: (2023)

Concentration of Contractive Stochastic Approximation: Additive and Multiplicative Noise
by: Chen, Zaiwei, et al.
Published: (2023)

Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose Theorem
by: Haque, Shaan Ul, et al.
Published: (2024)

Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise
by: Agrawal, Shubhada, et al.
Published: (2026)

Markov Chain Variance Estimation: A Stochastic Approximation Approach
by: Agrawal, Shubhada, et al.
Published: (2024)

Non-Asymptotic Convergence of Stochastic Iterative Algorithms: A Lyapunov Framework
by: Chen, Zaiwei, et al.
Published: (2026)

How Accurately Can a Gaussian Approximate Stochastic Approximation Iterates?
by: Haque, Shaan Ul, et al.
Published: (2026)

A General-Purpose Theorem for High-Probability Bounds of Stochastic Approximation with Polyak Averaging
by: Khodadadian, Sajad, et al.
Published: (2025)

Steady-State Behavior of Constant-Stepsize Stochastic Approximation: Gaussian Approximation and Tail Bounds
by: Wang, Zedong, et al.
Published: (2026)

Tail Distribution of Regret in Optimistic Reinforcement Learning
by: Khodadadian, Sajad, et al.
Published: (2025)

Convergence of Natural Policy Gradient for a Family of Infinite-State Queueing MDPs
by: Grosof, Isaac, et al.
Published: (2024)

Stochastic Approximation for Nonlinear Discrete Stochastic Control: Finite-Sample Bounds
by: Nguyen, Hoang Huy, et al.
Published: (2023)

Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning
by: Jhunjhunwala, Divyansh, et al.
Published: (2025)

A Non-Asymptotic Theory of Seminorm Lyapunov Stability: From Deterministic to Stochastic Iterative Algorithms
by: Chen, Zaiwei, et al.
Published: (2025)

Performance of NPG in Countable State-Space Average-Cost RL
by: Murthy, Yashaswini, et al.
Published: (2024)

High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
by: Armacki, Aleksandar, et al.
Published: (2023)

Federated Communication-Efficient Multi-Objective Optimization
by: Askin, Baris, et al.
Published: (2024)

FedAST: Federated Asynchronous Simultaneous Training
by: Askin, Baris, et al.
Published: (2024)

Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees
by: Armacki, Aleksandar, et al.
Published: (2024)

Debiasing Federated Learning with Correlated Client Participation
by: Sun, Zhenyu, et al.
Published: (2024)

Optimistic Reinforcement Learning with Quantile Objectives
by: Alipour-Vaezi, Mohammad, et al.
Published: (2025)

Improving the Convergence of Private Shuffled Gradient Methods with Public Data
by: Jiang, Shuli, et al.
Published: (2025)

Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems
by: Jali, Neharika, et al.
Published: (2024)

Almost Sure Convergence of Stochastic Approximation: An Interplay of Noise and Step Size
by: Nguyen, Quang Dinh Thien, et al.
Published: (2026)

Natural Policy Gradient for Average Reward Non-Stationary RL
by: Jali, Neharika, et al.
Published: (2025)

Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices
by: Woo, Jiin, et al.
Published: (2024)

Higher-Order Approximations of Sojourn Times in M/G/1 Queues via Stein's Method
by: Chatterjee, Bihan, et al.
Published: (2026)

Convergence Rate of the Join-the-Shortest-Queue System
by: Ma, Yuanzhe, et al.
Published: (2025)

Tail Bounds for Queues with Abandonment: Constant, Moderate, Large Deviations, and Efficient Concentration
by: Wang, Zedong, et al.
Published: (2026)

Sample Complexity of Average-Reward Q-Learning: From Single-agent to Federated Reinforcement Learning
by: Jiao, Yuchen, et al.
Published: (2026)

Parameter-Free Federated TD Learning with Markov Noise in Heterogeneous Environments
by: Naskar, Ankur, et al.
Published: (2025)

Adaptive Federated Learning via Dynamical System Model
by: Agarwal, Aayushya, et al.
Published: (2025)

FedECADO: A Dynamical System Model of Federated Learning
by: Agarwal, Aayushya, et al.
Published: (2024)

The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise
by: Liu, Shuze Daniel, et al.
Published: (2024)

A Heavy Traffic Theory of Matching Queues
by: Varma, Sushil Mahavir, et al.
Published: (2021)

FedFisher: Leveraging Fisher Information for One-Shot Federated Learning
by: Jhunjhunwala, Divyansh, et al.
Published: (2024)

FedSEA: Achieving Benefit of Parallelization in Federated Online Learning
by: Sahu, Harekrushna, et al.
Published: (2026)

SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
by: Mangold, Paul, et al.
Published: (2024)

Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models
by: Cho, Yae Jee, et al.
Published: (2024)

Convergence of Two-Timescale Markovian Stochastic Approximations with Applications in Reinforcement Learning
by: Mahadevan, Vagul, et al.
Published: (2026)