Feb. 15, 2024, 5:46 a.m. | Sid Wang, Ashish Shenoy, Pierce Chuang, John Nguyen

cs.CL updates on arXiv.org arxiv.org

arXiv:2305.03584v3 Announce Type: replace
Abstract: In recent years, Federated Learning (FL) has shown significant advancements in its ability to perform various natural language processing (NLP) tasks. This work focuses on applying personalized FL for on-device language modeling. Due to limitations of memory and latency, these models cannot support the complexity of sub-word tokenization or beam search decoding, resulting in the decision to deploy a closed-vocabulary language model. However, closed-vocabulary models are unable to handle out-of-vocabulary (OOV) words belonging to specific …

abstract arxiv complexity cs.ai cs.cl federated learning language language processing latency limitations memory modeling natural natural language natural language processing nlp personalized processing support tasks type work

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