# Federated Learning Semantic Communication in UAV Systems: PPO-Based Joint Trajectory and Resource Allocation Optimization

## TL;DR

This paper proposes a new method for UAV communication using semantic information and federated learning to reduce computational load and improve efficiency.

## Contribution

The novel contribution is a PPO-based framework for joint trajectory and resource allocation in UAV-assisted semantic communication using federated learning.

## Key findings

- Federated learning reduces computational burden on UAVs by offloading tasks to edge devices.
- The PPO-based algorithm minimizes energy consumption and task completion time while ensuring service fairness.
- Experimental results show improved quality-of-service and reduced resource consumption in UAV systems.

## Abstract

Semantic Communication (SC), driven by a deep learning (DL)-based “understand-before-transmit” paradigm, transmits lightweight semantic information (SI) instead of raw data. This approach significantly reduces data volume and communication overhead while maintaining performance, making it particularly suitable for UAV communications where the platform is constrained by size, weight, and power (SWAP) limitations. To alleviate the computational burden of semantic extraction (SE) on the UAV, this paper introduces federated learning (FL) as a distributed training framework. By establishing a collaborative architecture with edge users, computationally intensive tasks are offloaded to the edge devices, while the UAV serves as a central coordinator. We first demonstrate the feasibility of integrating FL into SC systems and then propose a novel solution based on Proximal Policy Optimization (PPO) to address the critical challenge of ensuring service fairness in UAV-assisted semantic communications. Specifically, we formulate a joint optimization problem that simultaneously designs the UAV’s flight trajectory and bandwidth allocation strategy. Experimental results validate that our FL-based training framework significantly reduces computational resource consumption, while the PPO-based algorithm approach effectively minimizes both energy consumption and task completion time while ensuring equitable quality-of-service (QoS) across all edge users.