Dr Maxime Vono
Lagrange Mathematics and Computing Research Center (Paris)
11th January 2022 - 4:00 pm - 5:00 pm (GST)
Bayesian Federated Learning
In the modern big data era, it has become commonplace to acquire and process a large amount of data at the edge nodes of a network. These nodes are typically devices (coined clients), such as IoT sensors or mobiles, coordinated by a central server. This rapid progress in data acquisition and storage has contributed to bringing out new paradigms regarding access to the data and their use in machine learning. Among these, we can cite partial device participation, limited upload bandwidth, data privacy issues, and heterogeneous data. Federated learning (FL) is a particular branch of machine learning which aims at addressing these new challenges.