Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908788287602688 |
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| author | Chen, Xingran NaderiAlizadeh, Navid Ribeiro, Alejandro Bidokhti, Shirin Saeedi |
| author_facet | Chen, Xingran NaderiAlizadeh, Navid Ribeiro, Alejandro Bidokhti, Shirin Saeedi |
| contents | We address real-time sampling and estimation of autoregressive Markovian sources in dynamic yet structurally similar multi-hop wireless networks. Each node caches samples from others and communicates over wireless collision channels, aiming to minimize time-average estimation error via decentralized policies. Due to the high dimensionality of action spaces and complexity of network topologies, deriving optimal policies analytically is intractable. To address this, we propose a graphical multi-agent reinforcement learning framework for policy optimization. Theoretically, we demonstrate that our proposed policies are transferable, allowing a policy trained on one graph to be effectively applied to structurally similar graphs. Numerical experiments demonstrate that (i) our proposed policy outperforms state-of-the-art baselines; (ii) the trained policies are transferable to larger networks, with performance gains increasing with the number of agents; (iii) the graphical training procedure withstands non-stationarity, even when using independent learning techniques; and (iv) recurrence is pivotal in both independent learning and centralized training and decentralized execution, and improves the resilience to non-stationarity. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_12662 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks Chen, Xingran NaderiAlizadeh, Navid Ribeiro, Alejandro Bidokhti, Shirin Saeedi Machine Learning Signal Processing We address real-time sampling and estimation of autoregressive Markovian sources in dynamic yet structurally similar multi-hop wireless networks. Each node caches samples from others and communicates over wireless collision channels, aiming to minimize time-average estimation error via decentralized policies. Due to the high dimensionality of action spaces and complexity of network topologies, deriving optimal policies analytically is intractable. To address this, we propose a graphical multi-agent reinforcement learning framework for policy optimization. Theoretically, we demonstrate that our proposed policies are transferable, allowing a policy trained on one graph to be effectively applied to structurally similar graphs. Numerical experiments demonstrate that (i) our proposed policy outperforms state-of-the-art baselines; (ii) the trained policies are transferable to larger networks, with performance gains increasing with the number of agents; (iii) the graphical training procedure withstands non-stationarity, even when using independent learning techniques; and (iv) recurrence is pivotal in both independent learning and centralized training and decentralized execution, and improves the resilience to non-stationarity. |
| title | Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2601.12662 |