CTTC organizes a five-day workshop for early-stage researchers including PhD students and postdocs, who are willing to dwell into the latest advances in artificial intelligence and machine learning and their interplay with telecommunication systems.

The workshop will be held at CTTC on May 13th – 17th, 2024 and will combine lectures, demos and practical hands-on the experimental facilities at CTTC as well as visits to local industrial partners and key players. Moreover, the attendees may have the possibility to present their work, interacting among themselves and with the senior researchers attending the event.

The workshop is supported by Teleco Renta and will fund 15 travel grants for international students to attend the workshop. The grant will cover the travel expenses and accommodation.  

The workshop is however open to any PhD student and postdoc. A minimum of 15 places will be reserved for international PhD students or early-stage postdocs applying for Teleco Renta support, the rest will be open until the workshop vacancies are completed.

The advancement of machine learning (ML) techniques, especially deep learning, reinforcement learning, and federated learning, has led to remarkable breakthroughs in a variety of application domains, including computer vision, natural language processing, Internet of Things (IoT), connected vehicles, environmental monitoring, and mobile network management. 

Despite the success of ML-related research, the main outcomes are grounded on the availability of big amount of data and high computational resources at a central (cloud) entity. However, modern information sources are distributed and generate data far from the cloud computing centres. Streaming and processing data at remote centres results to be highly inefficient and energy-consuming. Hence, distributed/decentralized learning has emerged, enabling a pervasive computing system where data can be processed directly or closer to the sources. In such a way, communication overhead, latency, memory requirements, energy consumption are reduced, and privacy concerns are mitigated.

The objective of this workshop is to bring together the state-of-the-art research approaches in distributed ML, energy-efficient algorithms and understand the interplay with communication systems to achieve pervasive and sustainable sensing, computing, and communication platforms, in which every agent has an active role in the learning process, regardless their capabilities and tasks to be accomplished.