A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimation

• URL: http://arxiv.org/abs/2606.20955v1
• Date: Thu, 18 Jun 2026 21:39:33 GMT
• Title: A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimation
• Abstract summary: This paper introduces a novel learning-based solution leveraging Gated Graph Neural Networks (GGNNs) for fast-convergent dynamic average estimation.<n>Taking advantage of the inherent structure of GGNNs, the proposed method models the estimation process as a distributed autoregressor.<n>Our approach significantly outperforms conventional model-based estimators in terms of both convergence speed and precision.
• Score: 12.229462448839369
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Dynamic average estimation is a critical problem in multi-agent systems, enabling agents to collaboratively estimate time-varying signals using only local information exchange. Traditional model-based approaches often face challenges related to convergence speed and sensitivity to network topology changes. This paper introduces a novel learning-based solution leveraging Gated Graph Neural Networks (GGNNs) for fast-convergent dynamic average estimation in a fully distributed manner. Taking advantage of the inherent structure of GGNNs, the proposed method models the estimation process as a distributed autoregressor, ensuring rapid convergence while maintaining stability. We incorporate a regularization term during training to enforce convergence guarantees and introduce an encoding-decoding mechanism to reduce communication overhead without sacrificing accuracy compared to standard GGNNs. Extensive numerical experiments demonstrate that our approach significantly outperforms conventional model-based estimators in terms of both convergence speed and precision, making it a promising alternative for multi-agent applications that require dynamic average estimation.

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