Postdoc at Umeå University, Sweden

The rapid development of autonomous systems, connected devices, and distributed applications poses several challenges in dealing with petabytes of data in diverse resource-constrained environments. Federated machine learning (FML) is a collaborative learning solution that can handle these problems without sharing data with centralized servers. However, several emerging threats target FML training, learning, and inference to fail or mislead models at early learning rounds, particularly backdoor and bitflip attack and defence strategies under-explored in FML. These results jeopardize achieving trustworthy performance for any downstream tasks. Therefore, this project envisions developing and validating attack and defence strategies in federated learning for limited and diverse non-iid (independent identically distributed) data under non-standard and adversarial settings, which are ideally suited for edge AI infrastructures. These goals can be achieved by inducing unique features in federated learning algorithms such as robust training, model restoration, trustworthy device selection, secure learning and aggregation, fault-tolerance against failures and attacks, multi-level regularizes, and massive device variations. The ambition is to validate them in classical non-standard settings and apply them to solutions for constraint environments (e.g., the Internet of Things (IoT) and robotic arms). Potentially, teaching up to a maximum of 20% can be included in the work tasks.

The position is funded by The Knut and Alice Wallenberg Foundation through The Wallenberg AI, Autonomous Systems and Software Program (WASP). WASP is Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry.

More details: https://wasp-sweden.org/

Monowar
—Courtesy—
Monowar Bhuyan, SMIEEE, WASP
Biträdande Universitetslektor, Department of Computing Science

Umeå University, Sweden
monowar@cs.umu.se, Office Phone: +46 90 786 67 05
https://people.cs.umu.se/~monowar/