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[2602.17271] Federated Latent Space Alignment for Multi-user Semantic Communications
Summary
This paper presents a novel approach to federated latent space alignment in multi-user semantic communications, addressing semantic mismatches in AI-native devices.
Why It Matters
As AI-driven communication becomes more prevalent, ensuring effective semantic understanding among devices is crucial. This research proposes a solution that enhances communication efficiency and task execution, which is vital for the development of collaborative AI systems.
Key Takeaways
- Introduces a method to align latent spaces in multi-user AI communications.
- Utilizes federated optimization for decentralized training of semantic equalizers.
- Demonstrates trade-offs between accuracy, communication overhead, and complexity.
- Focuses on enhancing mutual understanding in AI-driven tasks.
- Validates the approach through numerical results in goal-oriented scenarios.
Computer Science > Information Theory arXiv:2602.17271 (cs) [Submitted on 19 Feb 2026] Title:Federated Latent Space Alignment for Multi-user Semantic Communications Authors:Giuseppe Di Poce, Mario Edoardo Pandolfo, Emilio Calvanese Strinati, Paolo Di Lorenzo View a PDF of the paper titled Federated Latent Space Alignment for Multi-user Semantic Communications, by Giuseppe Di Poce and 3 other authors View PDF HTML (experimental) 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 communica...