Communication-Efficient Federated Learning for Large-Scale Multiagent Systems in ISAC: Data Augmentation With Reinforcement Learning

Integrated sensing and communication (ISAC) has attracted great attention with the gains of spectrum efficiency and deployment costs through the coexistence of sensing and communication functions. Meanwhile, federated learning (FL) has great potential to apply to large-scale multiagent systems (LSMAS) in ISAC due to the attractive privacy protection mechanism. Nonindependent identically distribution (non-IID) is a fundamental challenge in FL and seriously affects the convergence performance. To deal with the non-IID issue in FL, a data augmentation optimization algorithm (DAOA) is proposed based on reinforcement learning (RL), where an augmented dataset is generated based on a generative adversarial network (GAN) and the local model parameters are inputted into a deep Q-network (DQN) to learn the optimal number of augmented data. Different from the existing works that only optimize the training performance, the number of augmented data is also considered to improve the sample efficiency in the article. In addition, to alleviate the high-dimensional input challenge in DQN and reduce the communication overhead in FL, a lightweight model is applied to the client based on deep separable convolution (DSC). Simulation results indicate that our proposed DAOA algorithm acquires considerable performance with significantly fewer augmented data, and the communication overhead is reduced greatly compared with benchmark algorithms.

I. Introduction

Recently, wireless communication and radar sensing technology have achieved disruptive evolution, which consumes the majority of available spectrum resources. Meanwhile, the exponential growth of communication and sensing equipment has deepened the concerns about the available wireless spectrum. Fortunately, radar sensing and wireless communication modules have many similar characteristics, such as signal processing, hardware design, and channel model, which provide a satisfied foundation for the integration. Promoted by the mentioned above, integrated sensing and communication (ISAC) is proposed to improve spectrum efficiency, reduce deployment costs, and achieve integration gain [1], [2], [3]. Generally, current works on ISAC mainly contain sensing-enhanced communication and communication-assisted sensing. In terms of sensing-assisted communication, channel estimation parameters can enhance the transmitter to optimize the beamforming of wireless signal [4]. Furthermore, the overhead of beam alignment and communication delay can be reduced with the assistance of sensing parameters in the vehicle-to-everything (V2X) scenarios [5].