Universal Distributed Feature Extraction Complementary to Side Information via H-Score
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 sh...
| Authors | Yuan Xinjie, Dier Tang, Shaolun-Huang |
|---|---|
| Primary category | cs.IT |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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