- MSc Project
- Federated learning is a technique which allows training machine learning models in a distributed way without transferring the data from its source. It has thus potential applications in medical image analysis, where privacy and security issues are of great 0
- importance. Although there are examples of using federated approaches to analysis of medical images, there is still need for research in this area and for experiments in distributed environment.
The goal of the thesis will be to apply federated learning techniques to the problem of medical image segmentation. We plan to use public datasets such as echocardiography, coming from multiple sources. The analysis will be performed using distributed computing frameworks such as Flower or FedML, using distributed computing infrastructures such as PL-Grid or a public cloud service. In addition to evaluation of the learning process, the goal will be also to evaluate the performance of distributed computing environment. Further study will include also possible attacks and security of the developed solution. Other types of data and machine learning tasks can be considered for comparison as well.