# Universal Distributed Feature Extraction Complementary to Side Information via H-Score

arXiv: https://arxiv.org/abs/2610.16707 · HTML: https://mlsanity.com/paper/2610.16707/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 12 Oct 2026.

## Builds on (most cited first, 7 of 7)

- [Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)](https://mlsanity.com/paper/1812.11494/) (2018)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [An Efficient Approach to Informative Feature Extraction from Multimodal Data](https://mlsanity.com/paper/1811.08979/) (2018)
- [Neural Feature Learning in Function Space](https://mlsanity.com/paper/2309.10140/) (2023)
- [Sharp Regret Bounds and a Task-Covariance Correction for Spectral Representation Learning](https://mlsanity.com/paper/2609.15825/) (2026)

## Abstract (arXiv)

This paper considers a distributed estimation system where edge nodes transmit averaged features to a center node for parameter estimation. A key challenge arises when the center node possesses auxiliary side information: how to design local extractors that are universally optimal for any downstream task, while avoiding redundancy with the side information. To tackle this problem, we orthogonally decompose the joint function space and demonstrate in the weak-dependence regime that the modal decomposition of the canonical dependence kernel's conditional-additive component yields optimal, task-independent features. We construct a distributed nested H-score to learn this decomposition and show that, under a limited feature budget, the mean-squared error (MSE) gap to the best task-specific features is determined solely by the task's alignment with the discarded, least informative modes, and vanishes to leading order for every square-integrable task once the budget is sufficient.