Federated Q-Learning: Linear Regret Speedup with Low Communication Cost

• URL: http://arxiv.org/abs/2312.15023v2
• Date: Tue, 7 May 2024 23:31:54 GMT
• Title: Federated Q-Learning: Linear Regret Speedup with Low Communication Cost
• Authors: Zhong Zheng, Fengyu Gao, Lingzhou Xue, Jing Yang,
• Abstract summary: We propose two federated Q-Learning algorithms termed as FedQ-Hoeffding and FedQ-Bernstein. We show that the corresponding total regrets achieve a linear speedup compared with their single-agent counterparts when the time horizon is sufficiently large. Those results rely on an event-triggered synchronization mechanism between the agents and the server.
• Score: 4.380110270510058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we consider federated reinforcement learning for tabular episodic Markov Decision Processes (MDP) where, under the coordination of a central server, multiple agents collaboratively explore the environment and learn an optimal policy without sharing their raw data. While linear speedup in the number of agents has been achieved for some metrics, such as convergence rate and sample complexity, in similar settings, it is unclear whether it is possible to design a model-free algorithm to achieve linear regret speedup with low communication cost. We propose two federated Q-Learning algorithms termed as FedQ-Hoeffding and FedQ-Bernstein, respectively, and show that the corresponding total regrets achieve a linear speedup compared with their single-agent counterparts when the time horizon is sufficiently large, while the communication cost scales logarithmically in the total number of time steps $T$. Those results rely on an event-triggered synchronization mechanism between the agents and the server, a novel step size selection when the server aggregates the local estimates of the state-action values to form the global estimates, and a set of new concentration inequalities to bound the sum of non-martingale differences. This is the first work showing that linear regret speedup and logarithmic communication cost can be achieved by model-free algorithms in federated reinforcement learning.

Related papers

• The Sample-Communication Complexity Trade-off in Federated Q-Learning [31.644851830271755]
  We investigate the trade-off between sample and communication complexities for the widely used class of intermittent communication algorithms. We propose a new algorithm, called Fed-DVR-Q, which is the first federated Q-learning algorithm to simultaneously achieve order-optimal sample and communication complexities.
  arXiv  Detail & Related papers  (2024-08-30T03:03:03Z)
• Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost [4.895986534376972]
  We propose a novel model-free federated Q-learning algorithm, termed FedQ-Advantage. Our algorithm not only requires a lower logarithmic communication cost but also achieves an almost optimal regret, reaching the information bound up to a logarithmic factor and near-linear regret speedup compared to its single-agent counterpart when the time horizon is sufficiently large.
  arXiv  Detail & Related papers  (2024-05-29T06:26:52Z)
• Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization [65.85963235502322]
  Federated Adam (FedAdam) algorithms suffer from a threefold increase in uplink communication overhead. We propose a novel sparse FedAdam algorithm called FedAdam-SSM, wherein distributed devices sparsify the updates local model parameters and moment estimates. By minimizing the divergence bound between the model trained by FedAdam-SSM and centralized Adam, we optimize the SSM to mitigate the learning performance degradation caused by sparsification error.
  arXiv  Detail & Related papers  (2024-05-28T07:56:49Z)
• DASA: Delay-Adaptive Multi-Agent Stochastic Approximation [64.32538247395627]
  We consider a setting in which $N$ agents aim to speedup a common Approximation problem by acting in parallel and communicating with a central server. To mitigate the effect of delays and stragglers, we propose textttDASA, a Delay-Adaptive algorithm for multi-agent Approximation.
  arXiv  Detail & Related papers  (2024-03-25T22:49:56Z)
• Scheduling and Communication Schemes for Decentralized Federated Learning [0.31410859223862103]
  A decentralized federated learning (DFL) model with the gradient descent (SGD) algorithm has been introduced. Three scheduling policies for DFL have been proposed for communications between the clients and the parallel servers. Results show that the proposed scheduling polices have an impact both on the speed of convergence and in the final global model.
  arXiv  Detail & Related papers  (2023-11-27T17:35:28Z)
• Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining [119.89373423433804]
  Area Under Precision-Recall (AUPRC) was introduced as an effective metric. Serverless multi-party collaborative training can cut down the communications cost by avoiding the server node bottleneck. We propose a new ServerLess biAsed sTochastic gradiEnt (SLATE) algorithm to directly optimize the AUPRC.
  arXiv  Detail & Related papers  (2023-08-06T06:51:32Z)
• The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond [44.43850105124659]
  We consider federated Q-learning, which aims to learn an optimal Q-function by periodically aggregating local Q-estimates trained on local data alone. We provide sample complexity guarantees for both the synchronous and asynchronous variants of federated Q-learning. We propose a novel federated Q-learning algorithm with importance averaging, giving larger weights to more frequently visited state-action pairs.
  arXiv  Detail & Related papers  (2023-05-18T04:18:59Z)
• Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning [55.198301429316125]
  Machine learning (ML) is a key technique for big-data-driven modelling and analysis of massive Internet of Things (IoT) based intelligent and ubiquitous computing. For fast-increasing applications and data amounts, distributed learning is a promising emerging paradigm since it is often impractical or inefficient to share/aggregate data. This paper studies the problem of training an ML model over decentralized systems, where data are distributed over many user devices.
  arXiv  Detail & Related papers  (2022-02-07T15:04:15Z)
• Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity [57.275753974812666]
  Federated learning involves learning from data samples distributed across a network of clients while the data remains local. In this paper, we propose a novel straggler-resilient federated learning method that incorporates statistical characteristics of the clients' data to adaptively select the clients in order to speed up the learning procedure.
  arXiv  Detail & Related papers  (2020-12-28T19:21:14Z)
• Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge Computing [113.52575069030192]
  Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones and vehicles. Due to the limitations of communication costs and security requirements, it is of paramount importance to extract information in a decentralized manner instead of aggregating data to a fusion center. We consider the problem of learning model parameters in a multi-agent system with data locally processed via distributed edge nodes. A class of mini-batch alternating direction method of multipliers (ADMM) algorithms is explored to develop the distributed learning model.
  arXiv  Detail & Related papers  (2020-10-02T10:41:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.