Master Thesis Federated Learning Internet of Things; IoT) Systems

The DLR Institute of Communications and Navigation is dedicated to mission‑oriented research in selected areas of communications and navigation. Its work ranges from the theoretical foundations to the demonstration of new procedures and systems in a real environment and is embedded in DLR's Space, Aeronautics, Transport, Security and Digitalization programmes.

The Advanced Information Processing Group aims at applying state‑of‑the‑art theoretical results into real‑world applications within information processing systems. The expertise of the group ranges from quantum error correction to Smart Data Management, exploring cutting‑edge communication theories such as semantic communication and Age of Information, pushing the boundaries of data utilization and dissemination.

The thesis will focus on the study and optimization, by means of network simulations, of federated learning (FL) solutions for Internet of Things (IoT) applications. Specifically, the possibility to efficiently support FL over 3

GPP narrow band‑IoT (NB‑IoT), a core element of IoT connectivity in 4G, 5G and 6G systems, will be explored, with attention to MAC protocol aspects.

• Familiarize with the main aspects of 3
  
  GPP NB‑IoT at the MAC layer
• Familiarize with the ns-3 network simulator
• Study, by means of ns-3 simulations how FL algorithms perform over NB‑IoT
• Optimize the NB‑IoT protocol parameters to efficiently support FL in use‑cases of practical relevance

• Good knowledge of communication systems and signal processing, with particular emphasis on MAC level protocols
• Interest in machine learning and federated learning
• Programming skills (C/C++) and willingness to learn new tools
• Previous experience with event‑driven network simulations is a plus
• Ability to work independently and interest in interdisciplinary topics
• Good knowledge of English language