FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

Abstract

Coronavirus (COVID-19) has created an unprecedented global crisis because of its detrimental effect on the global economy and health. COVID-19 cases have been rapidly increasing, with no sign of stopping. As a result, test kits and accurate detection models are in short supply. Early identification of COVID-19 patients will help decrease the infection rate. Thus, developing an automatic algorithm that enables the early detection of COVID-19 is essential. Moreover, patient data are sensitive, and they must be protected to prevent malicious attackers from revealing information through model updates and reconstruction. In this study, we presented a higher privacy-preserving federated learning system for COVID-19 detection without sharing data among data owners. First, we constructed a federated learning system using chest X-ray images and symptom information. The purpose is to develop a decentralized model across multiple hospitals without sharing data. We found that adding the spatial pyramid pooling to a 2D convolutional neural network improves the accuracy of chest X-ray images. Second, we explored that the accuracy of federated learning for COVID-19 identification reduces significantly for non-independent and identically distributed (Non-IID) data. We then proposed a strategy to improve the model’s accuracy on Non-IID data by increasing the total number of clients, parallelism (client-fraction), and computation per client. Finally, for our federated learning model, we applied a differential privacy stochastic gradient descent (DP-SGD) to improve the privacy of patient data. We also proposed a strategy to maintain the robustness of federated learning to ensure the security and accuracy of the model.

Polarization networks around the SDGs in the press from 2020 to 2023

“…1) The assessment of public policies aimed at governance should prioritize the reduction of polarization in SDGs 2, 3, 9, 12, and 15. It is also important to design public policies aligned with SDGs 1,4,5,6,7,8,10,11,13,14,16, and 17 to ensure governability and attain effective governance.…”

“…Regarding the Sustainable Development Goals, each of the 17 SDGs refers to radical or moderate positions on education, health, and employment, although the Internet user polarization is closer to point four related to quality education since the theory indicates that it can be achieved if the parties involved establish projects in the short, medium or long term that allow them to collaborate and be critical [14]. In this sense, Internet user polarization contributes to quality education because it reveals that the parties involved are conditioned by the medium in which they disseminate their positions.…”

An FTwNB Shield: A Credit Risk Assessment Model for Data Uncertainty and Privacy Protection

“…For example, in the medical field, in order to solve the problem of performance degradation in medical image classification, Zhao et al [10] proposed a novel federatedlearning method for distributed information sharing (FedDIS) and verified the performance of the algorithm using the Alzheimer's disease MRI dataset. In order to detect COVID-19, Ho et al [11] used chest X-ray images and symptom information combined with a convolutional neural network to build a federated-learning system, which successfully improved the detection accuracy while ensuring that the data were not shared. Also, in order to detect COVID-19, Kandati et al [12] proposed a novel hybrid algorithm named the genetic clustered FL (Genetic CFL) and proved that the Genetic CFL method is superior to the traditional AI method.…”

“…When the computing server receives federated parameters from different bank clients, it will sum them to obtain the global parameter ′, as shown in (11). Assuming that the updated training set based on incremental learning is U ′ = {x 1 , x 2 , • • • , x N }, we need to extract the parameters we need to federate according to η = {η , η 2 , • • • , η 19 }.…”

A new federated learning-based wireless communication and client scheduling solution for combating COVID-19

“… [28] propose a data-free knowledge distillation method to solve unbalanced problem by incorporating user data in a data-free approach through a server learning lightweight generator and broadcasting it to the clients to generalize deviations using the learned knowledge. [29] build a federated learning framework using medical images and disease information to improve the accuracy of image classification by adding spatial pyramidal layers to convolutional neural networks. [30] propose a architecture based on convolutional neural networks and graph neural networks.…”