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FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks. (arXiv:2104.08815v3 [cs.CL] UPDATED)
cs.LG 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 language processing learning natural natural language natural language processing processing