A Federated Adversarial Learning Method for Biomedical Named Entity Recognition

Zhao, Hanyu; Yuan, Sha; Xie, Niantao; Jiahong, Leng,; Wang, Guoqiang

doi:10.1109/bibm52615.2021.9669728

Cited by 4 publications

(5 citation statements)

References 5 publications

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“…In [126], the authors applied DP mechanisms through feature selection based on the statistical methods in the FL model to enhance privacy, analyze patients' genomic data, and identify the risk of heart failure. Zhao et al [127] applied the DP technology in FL to ensure data security and privacy by adding Gaussian noise during the local training and model aggregation process. Li et al [128] developed a cost-effective and privacy-preserving FL framework by implementing the DP method for an IoHT Alzheimer's disease detection scheme.…”

Section: Discussionmentioning

confidence: 99%

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Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

et al. 2023

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Advancements in wearable medical devices using the IoT technology are shaping the modern healthcare system. With the emergence of the Internet of Healthcare Things (IoHT), efficient healthcare services can be provided to patients. Healthcare professionals have effectively used AI-based models to analyze the data collected from IoHT devices to treat various diseases. Data must be processed and analyzed while avoiding privacy breaches, in compliance with legal rules and regulations, such as the HIPAA and GDPR. Federated learning (FL) is a machine learning-based approach allowing multiple entities to train an ML model collaboratively without sharing their data. It is particularly beneficial in healthcare, where data privacy and security are substantial concerns. Even though FL addresses some privacy concerns, there is still no formal proof of privacy guarantees for IoHT data. Privacy-enhancing technologies (PETs) are tools and techniques designed to enhance the privacy and security of online communications and data sharing. PETs provide a range of features that help protect users’ personal information and sensitive data from unauthorized access and tracking. This paper comprehensively reviews PETs concerning FL in the IoHT scenario and identifies several key challenges for future research.

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Section: Discussionmentioning

confidence: 99%

“…Zhao et al [127] proposed federated adversarial learning (FAL) with biomedical named entity recognition (BioNER). The DP technology was also used to ensure data security and privacy by adding Gaussian noise during the local training and model aggregation process.…”

Section: Perturbation Methodsmentioning

confidence: 99%

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

et al. 2023

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“…In order to solve the problem of medical data island, Zhao et al. are dedicated to applying FL to biomedical named entity recognition [10]. They bridge inner and outer hospital information via vertical FL without transmitting data into one centre cloud server.…”

Section: Related Workmentioning

confidence: 99%

“…They proposed the federated adversarial learning (FAL) method making use of a modified structured pruning scheme and exploit an improved adversarial learning approach. In order to solve the problem of medical data island, Zhao et al are dedicated to applying FL to biomedical named entity recognition [10]. They bridge inner and outer hospital information via vertical FL without transmitting data into one centre cloud server.…”

Section: Usage Of Federated Learning For Various Of Tasksmentioning

confidence: 99%

A novel parameter decoupling approach of personalised federated learning for image analysis

Pang

Wang

2023

IET Computer Vision

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Given the importance of privacy protection in databases and other institutions, federated learning (FL) is used to benefit training machine learning models based on these decentralised and private data so as to address the growing vision tasks. However, for federated learning, statistical heterogeneity continues to be a major problem. Recently, plenty of personalised federated learning methods have been explored to solve the problem of statistical heterogeneity. The usage of trained base layers and the effect of feature extraction in personalised layers, however, are hardly considered in those methods that employ the learning personalised models approach. To address the problem of the statistical heterogeneity in image analysis, PCCFED, a personalised federated learning method utilizing the strategy of parameter decoupling is proposed. It should be emphasised that the authors’ personalised federated learning method decouples the personalised (P) layers into a connecting (C) layer and classifier (C) layer in order to enhance the effectiveness of feature learning for personalised layers. Further, an approach is proposed to fully use the base layers to adapt a personalised model based on the newly admitted institution's dataset through meta‐transfer. The performance of the proposed PCCFED on three datasets is evaluated under the practical non‐independent and identically distributed (non‐IID) setting. Extensive experiments demonstrate that compared with baseline methods, the proposed framework achieves the best performance in federated learning and fine‐tuning. Through FL, the investigation reveals a method to reduce statistical heterogeneity while protecting the institutions' privacy.

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“…If the central server is semi-trusted, DP is applied to the aggregated weights an FL setting to substitute traditional RNNs such as LSTM. Zhao et al[60] introduce Federated Adversarial Learning (FAL). FAL consists of two components: a pruning component to compress the BERT model from 12 layers to four and an adversarial training component to introduce noise to improve the model's generalization.…”

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confidence: 99%

FLightNER: A Federated Learning Approach to Lightweight Named-Entity Recognition

Abadeer¹

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We introduce FLightNER, a collaboratively-trained model in Federated Learning (FL) setting that extends an existing state-of-the-art Named-Entity Recognition (NER) model that uses prompt-tuning known as LightNER. To the best of our knowledge at the time of writing, this is the first work that adapts and evaluates prompt-tuning in an FL environment for NER. FLightNER allows the aggregation of only the trainable parameters of LightNER without model accuracy degradation and saves 10 GB per client by not aggregating the entire model parameters. Trainable-only aggregation enables more clients to join a federation without extending memory prerequisites of the central server.We evaluate our approach against two baselines using three diverse datasets with different distributions across up to seven clients in a federation. We empirically show that compared to the centrally-trained LightNER model, FLightNER outperforms it by 19% when evaluated on a medical dataset, VAERS, with label imbalance across clients. We also show that FLightNER matches the same performance of a centrallytrained LightNER on two balanced datasets: CoNLL and I2B2. Furthermore, we implement and evaluate a federated strategy that drops the outlier client at the farthest distance from the federation's average. We show that dropping the outlier client consistently enhances the performance for the majority of clients.

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A Federated Adversarial Learning Method for Biomedical Named Entity Recognition

Cited by 4 publications

References 5 publications

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

A novel parameter decoupling approach of personalised federated learning for image analysis

FLightNER: A Federated Learning Approach to Lightweight Named-Entity Recognition

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