Bell Labs Internship on Collaborative Learning with Differential Privacy and Resource Efficiency (PhD)

Location: Germany

In collaborative learning (CL) – for instance, federated learning and split learning – multiple clients collaboratively train a model while ensuring their data remains local and private. However, traditional CL has two severe issues. The first issue is that the privacy promises of traditional CL have been broken where an adversary can launch various types of attacks to either reverse-engineer the client data or infer sensitive properties of such data even when all client data is kept local. The second issue with traditional CL is that its communication overhead is significant, especially with a model of ever-growing size and with a large number of participating clients. Therefore, the task of preserving privacy while ensuring efficient communication and computation is a fundamental challenge in CL. 

This internship is to explore collaborative learning, differential privacy, as well as potential systems mechanisms like gradient compression. You will work towards creating a CL system that works for even large ML models, with tunable differential privacy and efficiency guarantees that self-adapt to the user needs and the underlying infrastructure constraints. Ideally, this project will lead to a publication at a top academic venue.