Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating
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 IN...
| Authors | Mohammad Reza Deylam Salehi |
|---|---|
| Primary category | cs.IT |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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