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Meta Federated Reinforcement Learning for Distributed Resource Allocation

Zelin Ji, Zhijin Qin, Xiaoming Tao

TL;DR

Analysis and numerical results demonstrate that the proposed MFRL framework accelerates the reinforcement learning process, decreases transmission overhead, and offloads computation, while outperforming the conventional decentralized reinforcement learning algorithm in terms of convergence speed and EE performance across various scenarios.

Abstract

In cellular networks, resource allocation is usually performed in a centralized way, which brings huge computation complexity to the base station (BS) and high transmission overhead. This paper explores a distributed resource allocation method that aims to maximize energy efficiency (EE) while ensuring the quality of service (QoS) for users. Specifically, in order to address wireless channel conditions, we propose a robust meta federated reinforcement learning (\textit{MFRL}) framework that allows local users to optimize transmit power and assign channels using locally trained neural network models, so as to offload computational burden from the cloud server to the local users, reducing transmission overhead associated with local channel state information. The BS performs the meta learning procedure to initialize a general global model, enabling rapid adaptation to different environments with improved EE performance. The federated learning technique, based on decentralized reinforcement learning, promotes collaboration and mutual benefits among users. Analysis and numerical results demonstrate that the proposed \textit{MFRL} framework accelerates the reinforcement learning process, decreases transmission overhead, and offloads computation, while outperforming the conventional decentralized reinforcement learning algorithm in terms of convergence speed and EE performance across various scenarios.

Meta Federated Reinforcement Learning for Distributed Resource Allocation

TL;DR

Analysis and numerical results demonstrate that the proposed MFRL framework accelerates the reinforcement learning process, decreases transmission overhead, and offloads computation, while outperforming the conventional decentralized reinforcement learning algorithm in terms of convergence speed and EE performance across various scenarios.

Abstract

In cellular networks, resource allocation is usually performed in a centralized way, which brings huge computation complexity to the base station (BS) and high transmission overhead. This paper explores a distributed resource allocation method that aims to maximize energy efficiency (EE) while ensuring the quality of service (QoS) for users. Specifically, in order to address wireless channel conditions, we propose a robust meta federated reinforcement learning (\textit{MFRL}) framework that allows local users to optimize transmit power and assign channels using locally trained neural network models, so as to offload computational burden from the cloud server to the local users, reducing transmission overhead associated with local channel state information. The BS performs the meta learning procedure to initialize a general global model, enabling rapid adaptation to different environments with improved EE performance. The federated learning technique, based on decentralized reinforcement learning, promotes collaboration and mutual benefits among users. Analysis and numerical results demonstrate that the proposed \textit{MFRL} framework accelerates the reinforcement learning process, decreases transmission overhead, and offloads computation, while outperforming the conventional decentralized reinforcement learning algorithm in terms of convergence speed and EE performance across various scenarios.
Paper Structure (17 sections, 1 theorem, 20 equations, 8 figures, 1 table, 2 algorithms)

This paper contains 17 sections, 1 theorem, 20 equations, 8 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related work
  3. Energy-Efficient Resource Allocation for Cellular Networks
  4. Decentralized Reinforcement Algorithms in Wireless Communications
  5. Reinforcement Algorithm for Jointly Resource Optimization
  6. Motivations and Contributions
  7. Federated Reinforcement Learning
  8. Meta Reinforcement Technique for Fast Adaptation and Robustness
  9. System Model
  10. Proposed Meta Federated Reinforcement Learning for Resource Allocation
  11. Neural Network Structure Design
  12. Policy Gradient in Meta-training
  13. Advantage Estimation and Loss Function Design
  14. Meta-Adapting Process
  15. Global Averaging of Local Models
  16. ...and 2 more sections

Key Result

Lemma 1

Given $P^{\pi_\theta}$ is a parameterized probability distribution over a random variable $o$, then $\mathbb{E}_{o\sim P^{\pi_\theta}}\left[{\nabla_{\theta} \log P^{\pi_\theta}(o)}\right] = 0.$

Figures (8)

  • Figure 1: The proposed MFRL framework. The local models are uploaded and averaged periodically.
  • Figure 2: The proposed PPO network structure for the MFRL framework.
  • Figure 3: meta-training reward over the meta-training episodes. The curve represents the sum reward the agent gets from different tasks.
  • Figure 4: Training performance comparison of the proposed algorithm and benchmarks in three different scenarios.
  • Figure 5: Testing snapshots of the proposed algorithm and benchmarks in three different scenarios.
  • ...and 3 more figures

Theorems & Definitions (2)

  • Lemma 1: Expected Grad-Log-Prob Lemma
  • proof