Web: http://arxiv.org/abs/2104.08815

May 9, 2022, 1:11 a.m. | Bill Yuchen Lin, Chaoyang He, Zihang Zeng, Hulin Wang, Yufen Huang, Christophe Dupuy, Rahul Gupta, Mahdi Soltanolkotabi, Xiang Ren, Salman Avestimehr

cs.CL updates on arXiv.org arxiv.org

Increasing concerns and regulations about data privacy and sparsity
necessitate the study of privacy-preserving, decentralized learning methods for
natural language processing (NLP) tasks. Federated learning (FL) provides
promising approaches for a large number of clients (e.g., personal devices or
organizations) to collaboratively learn a shared global model to benefit all
clients while allowing users to keep their data locally. Despite interest in
studying FL methods for NLP tasks, a systematic comparison and analysis is
lacking in the literature. Herein, we …

arxiv benchmarking federated learning language learning natural natural language natural language processing processing

Data Analyst, Patagonia Action Works

@ Patagonia | Remote

Data & Insights Strategy & Innovation General Manager

@ Chevron Services Company, a division of Chevron U.S.A Inc. | Houston, TX

Faculty members in Research areas such as Bayesian and Spatial Statistics; Data Privacy and Security; AI/ML; NLP; Image and Video Data Analysis

@ Ahmedabad University | Ahmedabad, India

Director, Applied Mathematics & Computational Research Division

@ Lawrence Berkeley National Lab | Berkeley, Ca

Business Data Analyst

@ MainStreet Family Care | Birmingham, AL

Assistant/Associate Professor of the Practice in Business Analytics

@ Georgetown University McDonough School of Business | Washington DC