Masters Thesis: Continuous, Active Federated Learning for Data Streams

As Sweden's national center for applied AI, we're on a mission to accelerate the use of AI to benefit our society, our competitiveness, and everyone living in Sweden. We drive impactful initiatives in areas such as healthcare, energy, and public services while pushing the boundaries of AI research in fields such as natural language processing, machine learning and AI security. Join us in harnessing the untapped value of AI to drive innovation and create sustainable value for Sweden.

We are now looking for an ambitious Master’s thesis student to join us at AI Sweden to work alongside other industrial partners (Zenseact, Scaleout, Ekkono AI) on a unique mix of AI research topics for a use case in automotive.

Background

“As the automotive industry continues work on AI-driven automation, ensuring that data can be leveraged for safer driving without compromising privacy or security is paramount. This project pioneers a new approach to real-time federated learning, enabling continuous AI adaptation while fully complying with increasing regulations. By tackling these challenges, we are not just advancing autonomous driving—we are shaping the future of safe and secure, intelligent mobility."

- Jonas Ekmark, Head of New Technology, Zenseact

The increasing use of edge devices in vehicles (sensors such as LiDAR, cameras, etc.) has led to massive data generation, creating challenges for real-time AI adaptation, data privacy, and security. Traditional Federated Learning (FL) methods are unsuitable for real-time data-streaming environments like autonomous vehicles with high requirements on privacy and security. This project aims to advance safe automated vehicles by enhancing AI-driven perception, situational awareness, and decision-making while mitigating privacy breaches and national security risks. We introduce continuous, active federated learning for data streams to equip FL to work with high amounts of constantly incoming real-world data at the edge. The results will inform regulatory discussions on AI-driven traffic safety, supporting the development of policies for secure, automated vehicles. Our proposed approach enables data-efficient continuous model adaptation without requiring stored data, reducing cybersecurity risks and misuse of collected images or sensitive location data. The overall project, in which the student will be developing their Masters thesis, is a joint collaboration between AI Sweden (coordinator), Zenseact (autonomous driving use case), Scaleout Systems (FL frameworks), and Ekkono Solutions (edge learning). By integrating industry-leading expertise in these fields, the project ensures mission-critical innovation for transportation systems.

Who we’re looking for

We are seeking a curious, independent, and self-driven MSc student who wants to work at the absolute frontier of AI research.

• Ongoing Master’s studies in Computer Science, Data Science, Engineering Physics, Complex Adaptive Systems, Machine Learning, or similar.

• You should be comfortable with Python and deep learning, and comfortable with the idea that an experiment might tell you something you did not expect.

• Prior experience with active learning, continuous / incremental learning, self-supervised learning is useful but not required; an interest to pursue these topics is.

At AI Sweden, we are committed to building diverse and inclusive teams. Some positions may be subject to export control regulations, which means that specific requirements may apply.