Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating

• URL: http://arxiv.org/abs/2605.23424v1
• Date: Fri, 22 May 2026 09:35:05 GMT
• Title: Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating
• Abstract summary: In-network learning (INL) trains distributed neural modules by exchanging latent activations and backpropagated errors over a communication graph.<n>This letter proposes Dijkstra-pruned INL, which removes non-tree links by retaining a capacity-aware shortest-path tree rooted at the fusion node.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In-network learning (INL) trains distributed neural modules by exchanging latent activations and backpropagated errors over a communication graph. This letter proposes Dijkstra-pruned INL (D-INL), which removes non-tree links by retaining a capacity-aware shortest-path tree rooted at the fusion node. To balance sparsity and predictive information, local routing (or aggregation) is modeled as a finite-rate stochastic gate with rate $R_g=I(Z; T)$. We derive a rate-distortion-generalization bound and validate the method on a reproducible distributed-classification experiment, where D-INL reduces training exchange by $70.4\%$ while preserving accuracy within the standard deviation of dense INL. Adding finite-rate regularization further reduces the estimated latent rate by $45.7\%$ relative to unregularized Dijkstra INL.

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