Vol. 1 No. 4 (2026)

Articles

Sparse Intergroup Signaling for Efficient Distributed Neural Representations

Published 2026-10-06

  • Manon Chevalier
    • ,
  • Valentin Dubois
    • ,
  • Nicolas Mathieu

Manon Chevalier
Graduate School of Computer Science, Université Paris-Saclay, Gif-sur-Yvette, Île-de-France, France

Valentin Dubois
Department of Computer Science, Faculty of Science and Engineering, Sorbonne Université, Paris, Île-de-France, France

Nicolas Mathieu
Graduate School of Computer Science, Université Paris-Saclay, Gif-sur-Yvette, Île-de-France, France

Artificial Intelligence and Intelligent Systems Research, Volume 1, Issue 4, 2026 cover

Abstract

The exponential growth in the scale of deep learning models has necessitated the adoption of distributed computing architectures, where neural representations are partitioned across multiple processing nodes. While this paradigm allows for unprecedented computational capabilities, it introduces a severe communication bottleneck, as the synchronization of dense feature maps and gradients across nodes consumes enormous bandwidth and energy. Drawing inspiration from biological neural networks, which exhibit highly localized dense connectivity coupled with extremely sparse long-range projections, this paper proposes a novel framework based on sparse intergroup signaling. We investigate the theoretical foundations and practical implementations of enforcing sparsity constraints on the communication channels between distinct functional groups of neurons in distributed artificial neural networks. By formulating a rigorous mathematical optimization problem that penalizes intergroup message density without degrading the underlying representation quality, we demonstrate that structural and functional sparsity can be learned end-to-end. Our comprehensive analysis covers the architectural design required to implement such sparsity, the algorithmic gating mechanisms for dynamic signal routing, and the resulting trade-offs between computational overhead and representation fidelity. Extensive evaluations on large-scale datasets reveal that the proposed sparse intergroup signaling mechanism reduces communication overhead by orders of magnitude while preserving the predictive accuracy and generalization capabilities of the distributed models. These findings offer a scalable pathway for deploying massive neural architectures across edge devices and resource-constrained cluster environments.

Keywords

Distributed Neural Networks, Sparse Signaling, Representation Learning, Communication Efficiency

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Author Biography

Manon Chevalier

Editorial correspondence on behalf of the authors

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