Federated Latent Space Alignment for Multi-user Semantic Communications

TL;DR

Numerical results validate the proposed approach in goal-oriented semantic communication, revealing key trade-offs among accuracy, communication overhead, complexity, and the semantic proximity of AI-native communication devices.

Abstract

Semantic communication aims to convey meaning for effective task execution, but differing latent representations in AI-native devices can cause semantic mismatches that hinder mutual understanding. This paper introduces a novel approach to mitigating latent space misalignment in multi-agent AI- native semantic communications. In a downlink scenario, we consider an access point (AP) communicating with multiple users to accomplish a specific AI-driven task. Our method implements a protocol that shares a semantic pre-equalizer at the AP and local semantic equalizers at user devices, fostering mutual understanding and task-oriented communication while considering power and complexity constraints. To achieve this, we employ a federated optimization for the decentralized training of the semantic equalizers at the AP and user sides. Numerical results validate the proposed approach in goal-oriented semantic communication, revealing key trade-offs among accuracy, com- munication overhead, complexity, and the semantic proximity of AI-native communication devices.

Figures (3)

• Figure 1: Overview of the proposed system model.
• Figure 2: Accuracy vs. $\zeta$ with SNR=20 dB.
• Figure 3: Alignment struggle as Network MSE and Accuracy vs. $\zeta$ for a 4$\times$4 MIMO channel with SNR=20 dB.