English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

While developing artificial intelligence (AI)-based algorithms to solveproblems, the amount of data plays a pivotal role - large amount of data helpsthe researchers and engineers to develop robust AI algorithms. In the case ofbuilding AI-based models for problems related to medical imaging, these dataneed to be transferred from the medical institutions where they were acquiredto the organizations developing the algorithms. This movement of data involvestime-consuming formalities like complying with HIPAA, GDPR, etc.There is also arisk of patients' private data getting leaked, compromising theirconfidentiality. One solution to these problems is using the Federated Learningframework.  Federated Learning (FL) helps AI models to generalize better and create arobust AI model by using data from different sources having differentdistributions and data characteristics without moving all the data to a centralserver. In our paper, we apply the FL framework for training a deep learningmodel to solve a binary classification problem of predicting the presence orabsence of COVID-19. We took three different sources of data and trainedindividual models on each source. Then we trained an FL model on the completedata and compared all the model performances. We demonstrated that the FL modelperforms better than the individual models. Moreover, the FL model performed atpar with the model trained on all the data combined at a central server. ThusFederated Learning leads to generalized AI models without the cost of datatransfer and regulatory overhead.

Application of Federated Learning in Building a Robust COVID-19 Chest X-ray Classification Model