Efficient Federated Learning - Michael Kamp

Data science and machine learning is taking the world by storm. Almost all theory and methods, however, are inherently flawed in such a basic way that it prevents them from being used in practice. Unlike what most papers assume, in many applications (e.g., autonomous driving, industrial machines, or healthcare) it is impossible or hugely impractical to gather all data into one place. This is not only due to privacy concerns, but the sheer size of data makes centralizing and processing it infeasible. Federated learning offers a solution: models are trained only locally and combined to create a well-performing joint model - without sharing data. Like many data science techniques, applying them in practice requires a high level of trust. However, giving a guarantee on the model quality, training and resource efficiency, bounding the communication, and ensuring data privacy is a huge undertaking. In this talk I will present efficient, theoretically sound, and practically useful methods for efficient federated machine learning, as well as identify important and exciting open problems.