LFU:online

In this lecture, students will gain in-depth understanding in areas of distributed learning to solve common challenges stemming across the cloud-to-edge continuum. In particular, different aspects of optimization and distributed learning will be reviewed to later deal with realistic use cases related to smart data and resource management. Lectures will be complemented by practical exercises in which the students apply the concepts given in the lecture using cutting-edge frameworks.

Distributed learning (e.g., federated learning, hybrid learning, P2P learning) and Optimization techniques (e.g., reinforcement learning), applied to problems such as task offloading, scheduling, data management, load balancing, moving edge devices, energy consumption, etc.

Presentations, practical work.