Niklaus(Yi) Liu

Niklaus(Yi) Liu

Now, he is a research assistant at James Yu Lab of SUSTech.

Posts by Tags

Blockchain

Leveraging blockchain to make machine learning models more accessible

8 minute read

Published:

Significant advances are being made in artificial intelligence, but accessing and taking advantage of the machine learning systems making these developments possible can be challenging, especially for those with limited resources. These systems tend to be highly centralized, their predictions are often sold on a per-query basis, and the datasets required to train them are generally proprietary and expensive to create on their own. Additionally, published models run the risk of becoming outdated if new data isn’t regularly provided to retrain them. Read more

EMDs

Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records (Reading Notes)

6 minute read

Published:

Electronic Medical Records (EMRs) data is often used in the development of machine learning algorithms to predict disease incidence, patient response to treatment, and other medical events. But so far, most of the algorithms are centralized, rarely considering non-identically independent distributed (non-IID) data, and rarely considering the privacy sensitivity of EMRs can complicate the learning process of data. Read more

Edge Computing

Federated Learning：Bringing Machine Learning to the edge with Kotlin and Android (Reading Notes)

3 minute read

Published:

With the promulgation of the General Data Protection Regulation, users are becoming more aware of their data values and privacy concerns. While anonymous technology can greatly solve the problem of privacy security, the way in which all data is sent to the central processor to train the machine learning model is always the cause of data security concerns. Read more

Federated Learning

Application of federated XGBoost in outlier detection

8 minute read

Published:

Federated learning technology is rapidly evolving, and the combination of other machine learning methods has become a popular privacy protection model. XGBoost is the “big killer” of machine learning algorithms. In June 2019, the paper “Secureboost: A lossless federated learning framework” proposed the Secureboost federated learning framework, which realized the application of Boosting integrated learning method in vertical federated learning. In another recent paper, “The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost”, the authors proposed a method of combining XGBoost with horizontal federayed learning, which was used for credit card transaction anomaly detection and obtained. A good detection effect, find out the privacy protection program to achieve the best training results. Read more

Leveraging blockchain to make machine learning models more accessible

8 minute read

Published:

Significant advances are being made in artificial intelligence, but accessing and taking advantage of the machine learning systems making these developments possible can be challenging, especially for those with limited resources. These systems tend to be highly centralized, their predictions are often sold on a per-query basis, and the datasets required to train them are generally proprietary and expensive to create on their own. Additionally, published models run the risk of becoming outdated if new data isn’t regularly provided to retrain them. Read more

Interpret Federated Learning with Shapley Values

5 minute read

Published:

In this paper authors investigate the model interpretation methods for Federated Learning, specifically on the measurement of feature importance of vertical Federated Learning where feature space of the data is divided into two parties, namely host and guest. For host party to interpret a single prediction of vertical Federated Learning model, the interpretation results, namely the feature importance, are very likely to reveal the protected data from guest party. Aunthors propose a method to balance the model interpretability and data privacy in vertical Federated Learning by using Shapley values to reveal detailed feature importance for host features and a unified importance value for federated guest features. Authors’ experiments indicate robust and informative results for interpreting Federated Learning models. Read more

Federated Learning for Medical Imaging

5 minute read

Published:

Nearly 153 exabytes of healthcare-related data were generated in 2013; this number will increase by 48% annually to reach 2,314 exabytes in 2020 [1], [2], [3]. While machine learning can benefit from this “big data” to generate state-of-the-art models, most healthcare data is hard to obtain due to legal, privacy, technical, and data-ownership challenges, especially among international institutions where HIPAA and GDPR concerns need to be addressed [3], [4]. Read more

Federated Learning：Bringing Machine Learning to the edge with Kotlin and Android (Reading Notes)

3 minute read

Published:

With the promulgation of the General Data Protection Regulation, users are becoming more aware of their data values and privacy concerns. While anonymous technology can greatly solve the problem of privacy security, the way in which all data is sent to the central processor to train the machine learning model is always the cause of data security concerns. Read more

Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records (Reading Notes)

6 minute read

Published:

Electronic Medical Records (EMRs) data is often used in the development of machine learning algorithms to predict disease incidence, patient response to treatment, and other medical events. But so far, most of the algorithms are centralized, rarely considering non-identically independent distributed (non-IID) data, and rarely considering the privacy sensitivity of EMRs can complicate the learning process of data. Read more

Towards Federated Learning at Scale：System Design (Reading Notes)

13 minute read

Published:

Now, Google has implemented the first product-level Federated Learning System and published the paper “Towards Federated Learning at Scale: System Design.” The paper further introduces the system design of federated learning and describes the design philosophy and existing challenges of this system. Moreover, Google put forward his solution. Read more

Federated Learning System

3 minute read

Published:

We use the vertically federated learning as an example to introduce the architecture of the federated learning system and to explain the detailed process of how it works. Read more

Federated Learning

14 minute read

Published:

The federative learning framework intends to make industries effectively and accurately use data across organizations while meeting the privacy, security and regulatory requirements, in addition to building more flexible and powerful models to enable business cooperation by using data collectively but without data exchange directly. Read more

Federated Learning System

Towards Federated Learning at Scale：System Design (Reading Notes)

13 minute read

Published:

Now, Google has implemented the first product-level Federated Learning System and published the paper “Towards Federated Learning at Scale: System Design.” The paper further introduces the system design of federated learning and describes the design philosophy and existing challenges of this system. Moreover, Google put forward his solution. Read more

Federated Learning System

3 minute read

Published:

We use the vertically federated learning as an example to introduce the architecture of the federated learning system and to explain the detailed process of how it works. Read more

Interpretable Machine Learning

Interpret Federated Learning with Shapley Values

5 minute read

Published:

In this paper authors investigate the model interpretation methods for Federated Learning, specifically on the measurement of feature importance of vertical Federated Learning where feature space of the data is divided into two parties, namely host and guest. For host party to interpret a single prediction of vertical Federated Learning model, the interpretation results, namely the feature importance, are very likely to reveal the protected data from guest party. Aunthors propose a method to balance the model interpretability and data privacy in vertical Federated Learning by using Shapley values to reveal detailed feature importance for host features and a unified importance value for federated guest features. Authors’ experiments indicate robust and informative results for interpreting Federated Learning models. Read more

Machine Learning

Application of federated XGBoost in outlier detection

8 minute read

Published:

Federated learning technology is rapidly evolving, and the combination of other machine learning methods has become a popular privacy protection model. XGBoost is the “big killer” of machine learning algorithms. In June 2019, the paper “Secureboost: A lossless federated learning framework” proposed the Secureboost federated learning framework, which realized the application of Boosting integrated learning method in vertical federated learning. In another recent paper, “The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost”, the authors proposed a method of combining XGBoost with horizontal federayed learning, which was used for credit card transaction anomaly detection and obtained. A good detection effect, find out the privacy protection program to achieve the best training results. Read more

Leveraging blockchain to make machine learning models more accessible

8 minute read

Published:

Significant advances are being made in artificial intelligence, but accessing and taking advantage of the machine learning systems making these developments possible can be challenging, especially for those with limited resources. These systems tend to be highly centralized, their predictions are often sold on a per-query basis, and the datasets required to train them are generally proprietary and expensive to create on their own. Additionally, published models run the risk of becoming outdated if new data isn’t regularly provided to retrain them. Read more

Interpret Federated Learning with Shapley Values

5 minute read

Published:

In this paper authors investigate the model interpretation methods for Federated Learning, specifically on the measurement of feature importance of vertical Federated Learning where feature space of the data is divided into two parties, namely host and guest. For host party to interpret a single prediction of vertical Federated Learning model, the interpretation results, namely the feature importance, are very likely to reveal the protected data from guest party. Aunthors propose a method to balance the model interpretability and data privacy in vertical Federated Learning by using Shapley values to reveal detailed feature importance for host features and a unified importance value for federated guest features. Authors’ experiments indicate robust and informative results for interpreting Federated Learning models. Read more

Federated Learning for Medical Imaging

5 minute read

Published:

Nearly 153 exabytes of healthcare-related data were generated in 2013; this number will increase by 48% annually to reach 2,314 exabytes in 2020 [1], [2], [3]. While machine learning can benefit from this “big data” to generate state-of-the-art models, most healthcare data is hard to obtain due to legal, privacy, technical, and data-ownership challenges, especially among international institutions where HIPAA and GDPR concerns need to be addressed [3], [4]. Read more

Federated Learning：Bringing Machine Learning to the edge with Kotlin and Android (Reading Notes)

3 minute read

Published:

With the promulgation of the General Data Protection Regulation, users are becoming more aware of their data values and privacy concerns. While anonymous technology can greatly solve the problem of privacy security, the way in which all data is sent to the central processor to train the machine learning model is always the cause of data security concerns. Read more

Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records (Reading Notes)

6 minute read

Published:

Electronic Medical Records (EMRs) data is often used in the development of machine learning algorithms to predict disease incidence, patient response to treatment, and other medical events. But so far, most of the algorithms are centralized, rarely considering non-identically independent distributed (non-IID) data, and rarely considering the privacy sensitivity of EMRs can complicate the learning process of data. Read more

Federated Learning System

3 minute read

Published:

We use the vertically federated learning as an example to introduce the architecture of the federated learning system and to explain the detailed process of how it works. Read more

Medical Imaging

Federated Learning for Medical Imaging

5 minute read

Published:

Nearly 153 exabytes of healthcare-related data were generated in 2013; this number will increase by 48% annually to reach 2,314 exabytes in 2020 [1], [2], [3]. While machine learning can benefit from this “big data” to generate state-of-the-art models, most healthcare data is hard to obtain due to legal, privacy, technical, and data-ownership challenges, especially among international institutions where HIPAA and GDPR concerns need to be addressed [3], [4]. Read more

XGBoost

Application of federated XGBoost in outlier detection

8 minute read

Published:

Federated learning technology is rapidly evolving, and the combination of other machine learning methods has become a popular privacy protection model. XGBoost is the “big killer” of machine learning algorithms. In June 2019, the paper “Secureboost: A lossless federated learning framework” proposed the Secureboost federated learning framework, which realized the application of Boosting integrated learning method in vertical federated learning. In another recent paper, “The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost”, the authors proposed a method of combining XGBoost with horizontal federayed learning, which was used for credit card transaction anomaly detection and obtained. A good detection effect, find out the privacy protection program to achieve the best training results. Read more