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cs.CL2026
Component-Aware Differential Privacy for Federated Multilingual Speech-LLMs
Jordi Luque, Fernando López, Aleix Sant
Per-layer differential privacy (DP) clipping improves gradient fidelity in federated learning by allocating per-matrix clipping budgets proportional to parameter count. We show tha…
cs.CL2026
EuroAlpaca: Task-Preserving Localisation of Instruction Data for European Languages
Aleix Sant, Jordi Luque, Carlos Escolano
Machine translation (MT) offers a scalable way to extend English instruction-tuning data to multiple languages, but it can distort task-critical constraints and required outputs, c…
cs.CL2026
Optimizing Multilingual LLMs via Federated Learning: A Study of Client Language Composition
Aleix Sant, Jordi Luque, Carlos Escolano
Federated Learning (FL) of Large Language Models (LLMs) in multilingual environments presents significant challenges stemming from heterogeneous language distributions across clien…