An Efficient Federated Learning Framework for Training Semantic Communication System

TL;DR

This paper proposes a federated learning framework for semantic communication systems that preserves privacy, reduces communication overhead, and demonstrates improved performance through extensive simulations.

Contribution

It introduces FedLol, a novel federated learning method tailored for semantic communication, addressing privacy, bandwidth, and resource constraints.

Findings

FedLol outperforms baseline methods in simulations.

Significant bandwidth savings achieved.

Enhanced privacy preservation in training.

Abstract

Semantic communication has emerged as a pillar for the next generation of communication systems due to its capabilities in alleviating data redundancy. Most semantic communication systems are built upon advanced deep learning models whose training performance heavily relies on data availability. Existing studies often make unrealistic assumptions of a readily accessible data source, where in practice, data is mainly created on the client side. Due to privacy and security concerns, the transmission of data is restricted, which is necessary for conventional centralized training schemes. To address this challenge, we explore semantic communication in a federated learning (FL) setting that utilizes client data without leaking privacy. Additionally, we design our system to tackle the communication overhead by reducing the quantity of information delivered in each global round. In this way, we can save significant bandwidth for resource-limited devices and reduce overall network traffic. Finally, we introduce a mechanism to aggregate the global model from clients, called FedLol. Extensive simulation results demonstrate the effectiveness of our proposed technique compared to baseline methods.