Federated Latent Space Alignment for Multi-user Semantic Communications
This work addresses semantic communication challenges for AI-native devices, but it appears incremental as it builds on existing federated and semantic communication methods.
The paper tackles the problem of semantic mismatches in multi-agent AI-native semantic communications by introducing a federated optimization approach for latent space alignment, achieving a balance between accuracy, communication overhead, complexity, and semantic proximity.
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.