Pinned Repositories
awesome-federated-learning
resources about federated learning and privacy in machine learning
blog
SpringBoot + Mybatis + thymeleaf 搭建的个人博客 http://www.54tianzhisheng.cn/
Coursera-ML-AndrewNg-Notes
吴恩达老师的机器学习课程个人笔记
csdn-download-resurces
👌CSDN下载频道优质源码集锦，动态展示地址：http://t.cn/RpjiHlF
da123
机器学习数据集导航
Deep-Learning-with-TensorFlow-book
深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.
Federated-Learning-for-Human-Mobility-Models
Thanks to the proliferation of smart devices, such as smartphones and wearables, which are equipped with computation, communication and sensing capabilities, a plethora of new location-based services and applications are available for the users at any time and everywhere. Understanding human mobility has gain importance to offer better services able to provide valuable products to the user whenever it's required. The ability to predict when and where individuals will go next allows enabling smart recommendation systems or a better organization of resources such as public transport vehicles or taxis. Network providers can predict future activities of individuals and groups to optimize network handovers, while transport systems can provide more vehicles or lines where required, reducing waiting time and discomfort to their clients. The representation of the movements of individuals or groups of mobile entities are called human mobility models. Such models replicate real human mobility characteristics, enabling to simulate movements of different individuals and infer their future whereabouts. The development of these models requires to collect in a centralized location, as a server, the information related to the users' locations. Such data represents sensitive information, and the collection of those threatens the privacy of the users involved. The recent introduction of federated learning, a privacy-preserving approach to build machine and deep learning models, represents a promising technique to solve the privacy issue. Federated learning allows mobile devices to contribute with their private data to the model creation without sharing them with a centralized server. In this thesis, we investigate the application of the federated learning paradigm to the field of human mobility modelling. Using three different mobility datasets, we first designed and developed a robust human mobility model by investigating different classes of neural networks and the influence of demographic data over models' performance. Second, we applied federated learning to create a human mobility model based on deep learning which does not require the collection of users' mobility traces, achieving promising results on two different datasets. Users' data remains so distributed over the big number of devices which have generated them, while the model is shared and trained among the server and the devices. Furthermore, the developed federated model has been the subject of different analyses including: the effects of sparse availability of the clients; The communication costs required by federated settings; The application of transfer-learning techniques and model refinement through federated learning and, lastly, the influence of differential privacy on the model’s prediction performance, also called utility
gcn
Implementation of Graph Convolutional Networks in TensorFlow
Generative-Adversarial-Networks-Projects
Generative Adversarial Networks Projects, published by Packt
gitxuexi
Kukuuuuu's Repositories
Kukuuuuu/awesome-federated-learning
resources about federated learning and privacy in machine learning
Kukuuuuu/blog
SpringBoot + Mybatis + thymeleaf 搭建的个人博客 http://www.54tianzhisheng.cn/
Kukuuuuu/Coursera-ML-AndrewNg-Notes
吴恩达老师的机器学习课程个人笔记
Kukuuuuu/csdn-download-resurces
👌CSDN下载频道优质源码集锦，动态展示地址：http://t.cn/RpjiHlF
Kukuuuuu/da123
机器学习数据集导航
Kukuuuuu/Deep-Learning-with-TensorFlow-book
深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.
Kukuuuuu/Federated-Learning-for-Human-Mobility-Models
Thanks to the proliferation of smart devices, such as smartphones and wearables, which are equipped with computation, communication and sensing capabilities, a plethora of new location-based services and applications are available for the users at any time and everywhere. Understanding human mobility has gain importance to offer better services able to provide valuable products to the user whenever it's required. The ability to predict when and where individuals will go next allows enabling smart recommendation systems or a better organization of resources such as public transport vehicles or taxis. Network providers can predict future activities of individuals and groups to optimize network handovers, while transport systems can provide more vehicles or lines where required, reducing waiting time and discomfort to their clients. The representation of the movements of individuals or groups of mobile entities are called human mobility models. Such models replicate real human mobility characteristics, enabling to simulate movements of different individuals and infer their future whereabouts. The development of these models requires to collect in a centralized location, as a server, the information related to the users' locations. Such data represents sensitive information, and the collection of those threatens the privacy of the users involved. The recent introduction of federated learning, a privacy-preserving approach to build machine and deep learning models, represents a promising technique to solve the privacy issue. Federated learning allows mobile devices to contribute with their private data to the model creation without sharing them with a centralized server. In this thesis, we investigate the application of the federated learning paradigm to the field of human mobility modelling. Using three different mobility datasets, we first designed and developed a robust human mobility model by investigating different classes of neural networks and the influence of demographic data over models' performance. Second, we applied federated learning to create a human mobility model based on deep learning which does not require the collection of users' mobility traces, achieving promising results on two different datasets. Users' data remains so distributed over the big number of devices which have generated them, while the model is shared and trained among the server and the devices. Furthermore, the developed federated model has been the subject of different analyses including: the effects of sparse availability of the clients; The communication costs required by federated settings; The application of transfer-learning techniques and model refinement through federated learning and, lastly, the influence of differential privacy on the model’s prediction performance, also called utility
Kukuuuuu/gcn
Implementation of Graph Convolutional Networks in TensorFlow
Kukuuuuu/Generative-Adversarial-Networks-Projects
Generative Adversarial Networks Projects, published by Packt
Kukuuuuu/gitxuexi
Kukuuuuu/hsqblog
Kukuuuuu/kuku
Kukuuuuu/library
个人书籍目录
Kukuuuuu/Location-based-Privacy-Protection
Location based services are increasingly popular in providing users with useful information about their hereabouts and recommending eateries and other services based on their location. These services depend on data utility (usability of the data) which is paramount in providing accurate recommendations and locations. However, the higher the data utility, the lower the user privacy. User location privacy depends on mechanisms such as cloaking, ‘data noise’ and data aggregation to hide the user in amongst other data points.
Kukuuuuu/markov-based-location-prediction
Kukuuuuu/mlforhealthlabpub
Machine Learning and Artificial Intelligence for Medicine.
Kukuuuuu/papers-with-video
Chrome extension that adds video explanations to research papers on arxiv.org
Kukuuuuu/Prediction-of-Future-location-using-Hidden-Markov-Model
Prediction of Future location using Hidden Markov Model
Kukuuuuu/RuoYi
若依的项目
Kukuuuuu/Security-Code-Query-System
一个简单的防伪码查询系统
Kukuuuuu/SPEX
Stay Point EXtractor
Kukuuuuu/TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
Kukuuuuu/tensorflow2_tutorials_chinese
tensorflow2中文教程，持续更新(当前版本:tensorflow2.0)，tag: tensorflow 2.0 tutorials
Kukuuuuu/xuexi