• MSc Project 
  • Although federated learning is a promising technique for analysis of medical images, as it may solve some security and privacy issues related to distributed data access, there is still a need to evaluate this technique in large-scale experiments in a distributed environment such as cloud infrastructure. 

The goal of the thesis will be to run large-scale experiments with federated learning on medical image classification tasks. We plan to use public datasets such as chest X-ray images, coming from multiple sources (countries, hospitals), and existing distributed machine learning frameworks. As the computing infrastructure, a public cloud and PL-Grid infrastructure will be used. Various metrics related to the distribution of data, its granularity and partitioning will be investigated, to understand their impacts on the both the efficiency of the learning process and the performance of the infrastructure. It will be also possible to extend the study to assess the impact of possible attacks and their mitigation strategies.