layout: post | title: “Recent preprints on cellular sheaves for causality, signal processing, and federated learning” | date: 2026-08-13 11:52:00 +0100 | categories: preprints

Below are four recent papers from our SPAICOM Lab at DIET, Sapienza University of Rome, on the application of sheaf-theoretic approaches to causality, signal processing, and federated learning:

  • D’Acunto, G., Grimaldi, E., Avino, V., Pandolfo, M. E., Di Nino, L., Barbarossa, S., & Di Lorenzo, P. (2026). Sheaf-Based Federated Representation Learning. [preprint]

  • Di Nino, L., D’Acunto, G., Barbarossa, S. & Di Lorenzo, P. (2026). Structured Sheaf Learning of Consistent Connection Graphs. [preprint]

  • D’Acunto, G., Di Nino, L., Di Lorenzo, P., & Barbarossa, S. (2026). Sheaf-theoretic Signal Processing on Graphs: Spectral Theory, Filtering, and Sampling. [preprint]

  • D’Acunto, G., Di Lorenzo, P., & Barbarossa, S. (2025). Networks of Causal Abstractions: A Sheaf-theoretic Framework [preprint]