Federated learning (FL) with differential privacy (DP) allows several organizations to learn a shared model from user data that only one organization is allowed to see. However, guaranteeing DP requires adding noise to gradients that scales with update dimensionality. While low-rank adaptation (LoRA) is a popular method for parameter-efficient fine-tuning, it is inherently sensitive to DP noise due to the multiplication operator. This noise amplification leads to significant utility degradation, hindering practical deployment in enterprise settings. To overcome this, we propose FedAnchor. Every round, FedAnchor captures compressed information using one of the LoRA matrices A and B, and then decompresses this to the complete adapted weight matrix via singular value decomposition (SVD) at the server. This relieves the model from DP noise exacerbation while maintaining full learning capacity. Experiments across multiple benchmarks demonstrate that FedAnchor matches the strongest baseline in non-private settings and outperforms it by 4.38% under stringent DP constraints (𝜖 = 3). At scale, FedAnchor maintains communication efficiency, computation robustness, and utility under strict privacy budgets, meeting the demands for enterprise-level cross-silo deployments.

This work investigates retrieval augmented generation as an efficient strategy for automatic context discovery in context-aware Automatic Speech Recognition (ASR) system, in order to improve transcription accuracy in the presence of rare or out-of-vocabulary terms. However, identifying the right context automatically remains an open challenge. This work proposes an efficient embedding-based retrieval approach for automatic context discovery in ASR. To contextualize its effectiveness, two alternatives based on large language models (LLMs) are also evaluated: (1) large language model (LLM)-based context generation via prompting, and (2) post-recognition transcript correction using LLMs. Experiments on the TED-LIUMv3, Earnings21 and SPGISpeech demonstrate that the proposed approach reduces WER by up to 17% (percentage difference) relative to using no-context, while the oracle context results in a reduction of up to 24.1%.