Optimising Scientific Collaboration with Federated Learning

Collaboration is a cornerstone of successful scientific research. Individual researchers have specific skills, instruments in their lab and resources available to them. This means a group of collaborators will each have unique data set, but also means that there are multiple ways they can contribute. Is it better to share your samples, your expertise, or your data? Is it possible to share nothing at all, and collaborate remotely by combining your results using federated learning?

In this project you will use a new federated learning package that includes federated data processing and fair aggregation, to compare different types of levels of collaboration between the digital twins of three hypothetical scientists (Alice, Bob and Charlie).
The code, digital twins and data sets will be provided, and the research will include:

  • extending the software functionalities
  • training neural networks under different scenarios
  • benchmarking and evaluation of different collaboration approaches
  • reproting and visualisation of results


Python programming and experience in data science and machine learning is essential (such as COMP3720, COMP4660, COMP4670, COMP6670, COMP8420). Familiarity with platforms such as Pytorch is desirable.

You are on Aboriginal land.

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

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