Federated Latent Space Alignment for Multi-user Semantic Communications

TL;DR

This paper proposes a federated learning-based method to align latent semantic spaces in multi-user AI-native communication, improving mutual understanding and task performance under resource constraints.

Contribution

It introduces a novel federated optimization framework for training semantic equalizers, addressing latent space misalignment in multi-user semantic communication.

Findings

Improved semantic alignment enhances task accuracy.

Trade-offs identified among communication overhead, complexity, and semantic proximity.

Numerical results validate the effectiveness of the proposed approach.

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.