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.

Exercises and project presentation.

Allocation of places in courses with a limited number of participants (PS, SE, VU, PJ)

In courses with a limited number of participants, course places are allocated as follows:

1. Students for whom the study duration would be extended due to the postponement are to be given priority.

2. If the criteria in no. 1 do not suffice, first, students for whom this course is part of a compulsory module are to be given priority, and second, students for whom this course is part of an elective module.

3. If the criteria in no. 1 and 2 do not suffice, the available places are drawn by random.

Curriculum MA Computer Science 2021W