Federated Latent Space Alignment for Multi-user Semantic Communications

• URL: http://arxiv.org/abs/2602.17271v1
• Date: Thu, 19 Feb 2026 11:18:58 GMT
• Title: Federated Latent Space Alignment for Multi-user Semantic Communications
• Authors: Giuseppe Di Poce, Mario Edoardo Pandolfo, Emilio Calvanese Strinati, Paolo Di Lorenzo,
• Abstract summary: We introduce a novel approach to mitigating latent space misalignment in multi-agent AI-native semantic communications.<n>Our method implements a protocol that shares a semantic pre-equalizer at the AP and local semantic equalizers at user devices.<n> Numerical results validate the proposed approach in goal-oriented semantic communication.
• Score: 8.625937174105642
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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