Component-Aware Differential Privacy for Federated Multilingual Speech-LLMs

Excerpt

arXiv:2609.11762v1 Announce Type: new Abstract: Per-layer differential privacy (DP) clipping improves gradient fidelity in federated learning by allocating per-matrix clipping budgets proportional to parameter count. We show that this recipe breaks for speech large language models (speech-LLMs), when the acoustic encoder and the language decoder differ by an order of magnitude in update norm. Single-pool per-layer methods suffer \emph{cross-component budget collapse}, dragging word error rate (W