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method ID: federatedlearning

Related Papers

Federated Learning of N-Gram Language Models

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractWe propose algorithms to train production-quality n-gram language models using federated learning. Federated learning is a distributed computation platform that can be used to train global models for portable devices …...

41734

Empirical Studies of Institutional Federated Learning For Natural Language Processing

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractFederated learning has sparkled new interests in the deep learning society to make use of isolated data sources from independent institutes. With the development of novel training tools, we have …...

32425

FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractUnlike other domains, medical texts are inevitably accompanied by private information, so sharing or copying these texts is strictly restricted. However, training a medical relation extraction model requires collecting these …...

30839

Federated Learning for Spoken Language Understanding

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractRecently, spoken language understanding (SLU) has attracted extensive research interests, and various SLU datasets have been proposed to promote the development. However, most of the existing methods focus on a …...

4056

Privacy-Preserving News Recommendation Model Learning

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractNews recommendation aims to display news articles to users based on their personal interest. Existing news recommendation methods rely on centralized storage of user behavior data for model training, which …...

32349

TextHide: Tackling Data Privacy in Language Understanding Tasks

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractAn unsolved challenge in distributed or federated learning is to effectively mitigate privacy risks without slowing down training or reducing accuracy. In this paper, we propose TextHide aiming at addressing …...

32356

A Secure and Efficient Federated Learning Framework for NLP

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractIn this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks for NLP. Existing solutions under this literature either consider a trusted aggregator or require …...

31326

Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractNews recommendation is critical for personalized news access. Most existing news recommendation methods rely on centralized storage of users’ historical news click behavior data, which may lead to privacy concerns …...

31877

Federated Chinese Word Segmentation with Global Character Associations

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

Chinese word segmentation (CWS) is a fun- damental task for Chinese information pro- cessing, which always suffers from out-of- vocabulary word issues, especially when it is tested on data from …...

33812

Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractAspect-based sentiment analysis (ABSA) predicts the sentiment polarity towards a particular aspect term in a sentence, which is an important task in real-world applications. To perform ABSA, the trained model …...

31788

Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractRecent advances in federated learning have demonstrated its promising capability to learn on decentralized datasets. However, a considerable amount of work has raised concerns due to the potential risks of …...

31636

Federated Learning with Noisy User Feedback

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractMachine Learning (ML) systems are getting increasingly popular, and drive more and more applications and services in our daily life. Thishas led to growing concerns over user privacy, since human …...

21213

Pretrained Models for Multilingual Federated Learning

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractSince the advent of Federated Learning (FL), research has applied these methods to natural language processing (NLP) tasks. Despite a plethora of papers in FL for NLP, no previous works …...

21354

Training Mixed-Domain Translation Models via Federated Learning

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractTraining mixed-domain translation models is a complex task that demands tailored architec- tures and costly data preparation techniques. In this work, we leverage federated learning (FL) in order to tackle …...

21222

Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractThis paper studies a new task of federated learning (FL) for semantic parsing, where multiple clients collaboratively train one global model without sharing their semantic parsing data. By leveraging data …...

37938

FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractThe inevitable private information in legal data necessitates legal artificial intelligence to study privacy-preserving and decentralized learning methods. Federated learning (FL) has merged as a promising technique for multiple participants …...

38664

FedPerC: Federated Learning for Language Generation with Personal and Context Preference Embeddings

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractFederated learning is a training paradigm that learns from multiple distributed users without aggregating data on a centralized server, promising the ability to deploy machine-learning to a diverse population of …...

33396

Practical Takes on Federated Learning with Pretrained Language Models

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractReal-world applications of language models entail data privacy constraints when learning from diverse data domains. Federated learning with pretrained language models for language tasks has been gaining attention lately but …...

33416