Federated Learning Meets Natural Language Processing: A Survey
- URL: http://arxiv.org/abs/2107.12603v1
- Date: Tue, 27 Jul 2021 05:07:48 GMT
- Title: Federated Learning Meets Natural Language Processing: A Survey
- Authors: Ming Liu, Stella Ho, Mengqi Wang, Longxiang Gao, Yuan Jin, He Zhang
- Abstract summary: Federated Learning aims to learn machine learning models from multiple decentralized edge devices (e.g. mobiles) or servers without sacrificing local data privacy.
Recent Natural Language Processing techniques rely on deep learning and large pre-trained language models.
- Score: 12.224792145700562
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Federated Learning aims to learn machine learning models from multiple
decentralized edge devices (e.g. mobiles) or servers without sacrificing local
data privacy. Recent Natural Language Processing techniques rely on deep
learning and large pre-trained language models. However, both big deep neural
and language models are trained with huge amounts of data which often lies on
the server side. Since text data is widely originated from end users, in this
work, we look into recent NLP models and techniques which use federated
learning as the learning framework. Our survey discusses major challenges in
federated natural language processing, including the algorithm challenges,
system challenges as well as the privacy issues. We also provide a critical
review of the existing Federated NLP evaluation methods and tools. Finally, we
highlight the current research gaps and future directions.
Related papers
- Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application [17.367710635990083]
We focus on natural language processing (NLP) and the role of large language models (LLMs)
This paper discusses advanced data preprocessing techniques and the use of frameworks like Hugging Face for implementing transformer-based models.
It highlights challenges such as handling multilingual data, reducing bias, and ensuring model robustness.
arXiv Detail & Related papers (2024-10-30T09:35:35Z) - A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus [71.77214818319054]
Natural language inference is a proxy for natural language understanding.
There is no publicly available NLI corpus for the Romanian language.
We introduce the first Romanian NLI corpus (RoNLI) comprising 58K training sentence pairs.
arXiv Detail & Related papers (2024-05-20T08:41:15Z) - Natural Language Processing for Dialects of a Language: A Survey [56.93337350526933]
State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets.
This survey delves into an important attribute of these datasets: the dialect of a language.
Motivated by the performance degradation of NLP models for dialectic datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches.
arXiv Detail & Related papers (2024-01-11T03:04:38Z) - Surveying the Landscape of Text Summarization with Deep Learning: A
Comprehensive Review [2.4185510826808487]
Deep learning has revolutionized natural language processing (NLP) by enabling the development of models that can learn complex representations of language data.
Deep learning models for NLP typically use large amounts of data to train deep neural networks, allowing them to learn the patterns and relationships in language data.
Applying deep learning to text summarization refers to the use of deep neural networks to perform text summarization tasks.
arXiv Detail & Related papers (2023-10-13T21:24:37Z) - Meta Learning for Natural Language Processing: A Survey [88.58260839196019]
Deep learning has been the mainstream technique in natural language processing (NLP) area.
Deep learning requires many labeled data and is less generalizable across domains.
Meta-learning is an arising field in machine learning studying approaches to learn better algorithms.
arXiv Detail & Related papers (2022-05-03T13:58:38Z) - Reinforced Iterative Knowledge Distillation for Cross-Lingual Named
Entity Recognition [54.92161571089808]
Cross-lingual NER transfers knowledge from rich-resource language to languages with low resources.
Existing cross-lingual NER methods do not make good use of rich unlabeled data in target languages.
We develop a novel approach based on the ideas of semi-supervised learning and reinforcement learning.
arXiv Detail & Related papers (2021-06-01T05:46:22Z) - FedNLP: A Research Platform for Federated Learning in Natural Language
Processing [55.01246123092445]
We present the FedNLP, a research platform for federated learning in NLP.
FedNLP supports various popular task formulations in NLP such as text classification, sequence tagging, question answering, seq2seq generation, and language modeling.
Preliminary experiments with FedNLP reveal that there exists a large performance gap between learning on decentralized and centralized datasets.
arXiv Detail & Related papers (2021-04-18T11:04:49Z) - A Survey on Recent Approaches for Natural Language Processing in
Low-Resource Scenarios [30.391291221959545]
Deep neural networks and huge language models are becoming omnipresent in natural language applications.
As they are known for requiring large amounts of training data, there is a growing body of work to improve the performance in low-resource settings.
Motivated by the recent fundamental changes towards neural models and the popular pre-train and fine-tune paradigm, we survey promising approaches for low-resource natural language processing.
arXiv Detail & Related papers (2020-10-23T11:22:01Z) - Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer [64.22926988297685]
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP)
In this paper, we explore the landscape of introducing transfer learning techniques for NLP by a unified framework that converts all text-based language problems into a text-to-text format.
arXiv Detail & Related papers (2019-10-23T17:37:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.