Student Projects

The following projects are available for Master and Bachelor students. They are performed in close collaboration with an experienced member of the lab. Apply for a project by sending an email to the contact mentioned for the project.

You may also suggest new projects, ideally close enough to our ongoing, or previously completed projects. In that case, you will have to convince Anne-Marie Kermarrec that it is worthwhile, of reasonable scope, and that someone in the lab can mentor you!

Collaborative Inference

Master’s thesis or Master’s semester project: 12 credits
Contact: Akash Dhasade ([email protected])

Recently, collaborative learning (Federated Learning (FL) and Decentralized Learning (DL)) evolved as an attractive alternative to address the need of scalability and data privacy. While attractive, these approaches bear huge communication costs which has been the primary interest of research. The goal of any learning however was singular and unmodified — to achieve good performance on test samples. In this project, we try to seek this goal directly. We consider a setting where nodes are decentralized, possess data, but this time instead of collaborating to train, they collaborate to infer. This shift of purview brings new benefits and reduces significantly the systems cost involved in training. The project concerns investigating techniques and algorithms to perform collaborative inference.

Required Technical Skills: Strong PyTorch and Python knowledge, machine learning and distributed systems.

Important note: This is no simple engineering project with a fixed goal. We are looking for motivated students with a research attitude. Only apply if you deem yourself fit.