Differentiated Federated Reinforcement Learning Based Traffic Offloading on Space-Air-Ground Integrated Networks

TL;DR

This paper introduces a novel differentiated federated reinforcement learning approach, DFSAC, for optimizing traffic offloading in the complex, heterogeneous SAGIN environment, significantly improving network performance metrics.

Contribution

It proposes the DFSAC algorithm that models traffic offloading as a DEC-POMDP and incorporates a global trend model for enhanced policy learning in SAGIN.

Findings

DFSAC outperforms traditional federated RL in throughput.

DFSAC reduces packet loss rate.

DFSAC decreases packet delay.

Abstract

The Space-Air-Ground Integrated Network (SAGIN) plays a pivotal role as a comprehensive foundational network communication infrastructure, presenting opportunities for highly efficient global data transmission. Nonetheless, given SAGIN's unique characteristics as a dynamically heterogeneous network, conventional network optimization methodologies encounter challenges in satisfying the stringent requirements for network latency and stability inherent to data transmission within this network environment. Therefore, this paper proposes the use of differentiated federated reinforcement learning (DFRL) to solve the traffic offloading problem in SAGIN, i.e., using multiple agents to generate differentiated traffic offloading policies. Considering the differentiated characteristics of each region of SAGIN, DFRL models the traffic offloading policy optimization process as the process of solving the Decentralized Partially Observable Markov Decision Process (DEC-POMDP) problem. The paper proposes a novel Differentiated Federated Soft Actor-Critic (DFSAC) algorithm to solve the problem. The DFSAC algorithm takes the network packet delay as the joint reward value and introduces the global trend model as the joint target action-value function of each agent to guide the update of each agent's policy. The simulation results demonstrate that the traffic offloading policy based on the DFSAC algorithm achieves better performance in terms of network throughput, packet loss rate, and packet delay compared to the traditional federated reinforcement learning approach and other baseline approaches.